#  ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-
Augmented Large Language Models

Yuxiang Zhang${}^{\textnormal{1}}$111Equal contribution. Jing
Chen${}^{\textnormal{2}}$111Equal contribution. Junjie
Wang${}^{\textnormal{1}}$111Equal contribution. Yaxin Liu${}^{\textnormal{3}}$
Cheng Yang${}^{\textnormal{4}}$ Chufan Shi${}^{\textnormal{3}}$ Xinyu
Zhu${}^{\textnormal{3}}$ Zihao Lin${}^{\textnormal{5}}$ Hanwen
Wan${}^{\textnormal{6}}$
Yujiu Yang${}^{\textnormal{3}}$ Tetsuya Sakai${}^{\textnormal{1}}$ Tian
Feng${}^{\textnormal{2}}$222Corresponding Author. Hayato
Yamana${}^{\textnormal{1}}$222Corresponding Author.
${}^{\textnormal{1}}$Waseda University ${}^{\textnormal{2}}$Zhejiang
University ${}^{\textnormal{3}}$Tsinghua University
${}^{\textnormal{4}}$CUHK ${}^{\textnormal{5}}$Virginia Tech
${}^{\textnormal{6}}$CUHK, Shenzhen
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>
https://github.com/ToolBeHonest/ToolBeHonest

###### Abstract

Tool-augmented large language models (LLMs) are rapidly being integrated into
real-world applications. Due to the lack of benchmarks, the community still
needs to fully understand the hallucination issues within these models. To
address this challenge, we introduce a comprehensive diagnostic benchmark,
ToolBH. Specifically, we assess the LLM’s hallucinations through two
perspectives: depth and breadth. In terms of depth, we propose a multi-level
diagnostic process, including (1) solvability detection, (2) solution
planning, and (3) missing-tool analysis. For breadth, we consider three
scenarios based on the characteristics of the toolset: missing necessary
tools, potential tools, and limited functionality tools. Furthermore, we
developed seven tasks and collected $700$ evaluation samples through multiple
rounds of manual annotation. The results show the significant challenges
presented by the ToolBH benchmark. The current advanced models Gemini-1.5-Pro
and GPT-4o only achieve a total score of $45.3$ and $37.0$, respectively, on a
scale of $100$. In this benchmark, larger model parameters do not guarantee
better performance; the training data and response strategies also play a
crucial role in tool-enhanced LLM scenarios. Our diagnostic analysis indicates
that the primary reason for model errors lies in assessing task solvability.
Additionally, open-weight models suffer from performance drops with verbose
replies, whereas proprietary models excel with longer reasoning.

ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-
Augmented Large Language Models

Yuxiang Zhang${}^{\textnormal{1}}$111Equal contribution. Jing
Chen${}^{\textnormal{2}}$111Equal contribution. Junjie
Wang${}^{\textnormal{1}}$111Equal contribution. Yaxin Liu${}^{\textnormal{3}}$
Cheng Yang${}^{\textnormal{4}}$ Chufan Shi${}^{\textnormal{3}}$ Xinyu
Zhu${}^{\textnormal{3}}$ Zihao Lin${}^{\textnormal{5}}$ Hanwen
Wan${}^{\textnormal{6}}$ Yujiu Yang${}^{\textnormal{3}}$ Tetsuya
Sakai${}^{\textnormal{1}}$ Tian Feng${}^{\textnormal{2}}$222Corresponding
Author. Hayato Yamana${}^{\textnormal{1}}$222Corresponding Author.
${}^{\textnormal{1}}$Waseda University ${}^{\textnormal{2}}$Zhejiang
University ${}^{\textnormal{3}}$Tsinghua University ${}^{\textnormal{4}}$CUHK
${}^{\textnormal{5}}$Virginia Tech ${}^{\textnormal{6}}$CUHK, Shenzhen
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>https://github.com/ToolBeHonest/ToolBeHonest

##  1 Introduction

To replicate human intelligence in artificial general intelligence (AGI),
recent studies suggest that a promising solution is enabling large language
models (LLMs) to use tools to handle diverse complex scenarios Mialon et al.
(2023). For instance, tool-augmented LLMs can employ various APIs or coding
tools to tackle intricate tasks, such as mathematical reasoning. However, as
illustrated in Fig. 1, several hallucinatory phenomenons arise when these LLMs
attempt to use tools.

Figure 1:  Hallucinations (red) and expectations (blue) of the LLM’s response
to the use of the task for tools that do not have a correct answer. The
problem example is taken from the AgentBoard Ma et al. (2024) Tool-Query
dataset, tested with ChatGPT OpenAI (2023b). Wrong Tool reflects a common
situation where LLM uses a provided wrong tool as the final answer; Non-
existent Tool, on the other hand, is an example of hallucination.

Specifically, the LLMs might utilize the wrong tools or offer solutions with
non-existent tools. This occurs because LLMs cannot discern whether a problem
is solvable with the tools at hand, and instead, they use fictitious tools,
hindering progress in developing AGI models. Therefore, a pressing issue is
assessing the hallucination phenomenon when LLMs utilize tools.

However, current benchmarks for tool-based tasks presuppose the provision of
complete and usable tools by users, primarily focusing on evaluating how LLMs
utilize these tools Liu et al. (2023); Qin et al. (2023); Ma et al. (2024).
For example, AgentBench Liu et al. (2023) and ToolBench Qin et al. (2023) aim
to introduce a broad array of tasks, and AgentBoard Ma et al. (2024) evaluate
the multi-step tool planning abilities of these models. Although MetaTool
Huang et al. (2023) considers incomplete lists of tools provided by users, it
still focuses solely on determining whether a task can be completed.
Therefore, to ensure a comprehensive evaluation of the various hallucinatory
phenomena in tool usage, we develop the ToolBH benchmark. It is a multi-level
hallucination diagnostic benchmark for tool-augmented LLMs. Inspired by
medical diagnostic procedures Wu et al. (2023) and existing hallucination
benchmarks Ji et al. (2023); Liu et al. (2022), ToolBH systematically assesses
hallucinations from perspectives of depth and breadth (Details in Sec. 3). In
the assessment of hallucination depth, we introduce a multi-level diagnostic
procedure framework that incorporates (1) solvability detection, (2) solution
planning, and (3) missing-tool analysis. The solvability detection task
assesses whether the use query can be addressed with the given toolset. After
that, the solution planning task requires the LLM to propose a plan of tools
tailored to address the task sequentially. In the end, the missing-tool
analysis task builds upon its predecessor by requiring the LLM to delineate
how the tools address the specific demands in each step. For tasks considered
unsolvable, the LLM is tasked with identifying and elaborating on the
functionalities of the missing tools, thereby offering insights to aid in
completing the toolkit. On the side of breadth, we consider three critical
scenarios from the perspective of toolset features: missing necessary tools,
potential tools, and limited functionality tools. Furthermore, we design seven
tasks across these scenarios, including iterative calls and optimal tool
selection tasks. Finally, we collect $700$ samples (user query, tool set,
solution) across $7$ tasks through multiple rounds of human annotations and
establish an evaluation metric for each diagnostic task (Details in Sec. 3.2).
We conduct experiments on $14$ LLMs using the ToolBH benchmark, which includes
$7$ proprietary and $7$ open-weight models. Our analysis reveals that despite
some open-weight models achieving competitive performance compared to
proprietary counterparts in general tasks Beeching et al. (2023); Contributors
(2023), they exhibit substantial gaps in unsolvable scenarios presented by the
ToolBH benchmark. Notably, the best-performing open-weight model, Llama-3-70B,
still trails behind the proprietary models, with only 32% of the total score
of Gemini-1.5-Pro Reid et al. (2024) and 40% of GPT-4o OpenAI (2024),
indicating considerable room for improvement in open-weight models. A detailed
error analysis shows a variety of error patterns. Proprietary models tend to
exhibit fewer instances of using non-existent tools, suggesting better tool
recognition capabilities. However, they also showed higher rates of
instrumental reasoning errors, reflecting problems comprehending the task’s
solution sequence and reasoning logic. On the other hand, open-weight models
suffer more from solvability hallucinations, often misjudging the complexity
and feasibility of tasks. This highlights a critical area for enhancement in
model reasoning.

Our findings demonstrate the significance of the ToolBH benchmark in
identifying and addressing deficiencies in LLMs. The benchmark provides
detailed analysis and valuable insights into specific error patterns under
unsolvable LLM tool usage scenarios, offering guidance for future research.

##  2 Related Work

###  2.1 Tool-based Benchmarks

Recent advancements in LLMs have highlighted their potential when augmented
with external tools, prompting the development of several benchmarks to assess
their tool-using capability. For example, API-Bank Li et al. (2023b),
ToolBench Qin et al. (2023), AgentBench Liu et al. (2023) and AgentBoard Ma et
al. (2024) evaluate LLMs’ capabilities in planning, retrieving, and calling
APIs across various tasks. Moreover, ToolQA Zhuang et al. (2023) introduces a
dataset for assessing tool-using LLMs by question answering, distinguishing
between internal knowledge and external tool usage. However, these benchmarks
operate under a stringent assumption: users will provide a list comprising all
requisite tools and describe each explicitly. This assumption rarely holds in
real-world scenarios. Few benchmarks Huang et al. (2023) incorporate a
reliability test, which evaluates whether the model can appropriately return
“None” when necessary tools are absent from the list. This approach fails to
address significant gaps, such as exploring the reasons behind the output of
“None”. To address these challenges, we introduce ToolBH, a comprehensive
multi-level hallucination diagnostic benchmark. ToolBH evaluates the tool-
using capabilities of models across diverse scenarios, further investigating
hallucinations to assist the community in identifying underlying causes.

###  2.2 Evaluating Hallucinations in LLMs

In LLMs, hallucination occurs when the output is inconsistent with the input,
contradicts established knowledge, or cannot be verified against factual data
Zhang et al. (2023). This phenomenon challenges the reliability and
credibility of LLMs, particularly in real-world applications.

To evaluate such hallucinations, current benchmarks are introduced, which are
based on the discriminative and generative capacities of LLMs. For instance,
TruthfulQA Lin et al. (2022) evaluates the truthfulness of generated
responses. In assessing the ability to differentiate between real and
hallucinatory statements, HaluEval Li et al. (2023a) focuses on whether the
model can accurately identify hallucinatory content within state information.
Moreover, FACTOR Muhlgay et al. (2024) investigates whether the model tends to
assign higher likelihoods to factual statements over non-factual ones,
examining the bias towards recognizing truth. However, these benchmarks only
assess the knowledge of large models without considering the interaction with
tools. Therefore, referencing their generative and discriminative approaches,
as well as the methods used in code Zhu et al. (2023) and medical diagnosis Wu
et al. (2023), we propose a multi-level benchmark to explore the phenomena of
hallucinations in the tool-augmented LLMs.

##  3 The ToolBH Benchmark

Figure 2: The pipeline of ToolBH benchmark. In-breadth, we examine three
scenarios (MNT, PT, LFT) that could induce hallucinations from the perspective
of the toolset. We employ an in-depth, multi-level evaluation (solvability
detection, solution planning, and missing-tool analysis) to diagnose the
reasons for hallucinations in LLM.

###  3.1 Design Philosophy

To comprehensively assess the hallucination phenomena encountered by tool-
enhanced large models, we design tasks and data processing procedures from
both depth and breadth perspectives of hallucination evaluation. Accordingly,
our workflow adheres to the following design philosophy:

  * •

Unsolvability in real-world tasks.

  * •

In-depth: Multi-level hallucination diagnosis.

  * •

In-breadth: Hallucination-inducing scenarios.

Unsolvability in real-world tasks. In our observations of real-world
applications, hallucinations frequently occur when LLMs attempt to address
tool-use requests that they believe are solvable but are inherently
unsolvable. We summarize these instances as unsolvable long-tail tool-using
tasks.

Multi-level hallucination diagnosis. We aim to conduct an in-depth analysis of
hallucinations experienced by LLMs when tackling unsolvable tasks to explore
the underlying mechanisms. To achieve this objective, we decompose these
unsolvable tasks into three progressive stages. This decomposition is based on
the level of detail of the responses, as detailed in Sec. 3.2.

Hallucination-inducing scenarios. In tool-based tasks, the pivotal element is
the tool-related information, including toolsets and tool descriptions.
Therefore, we consider two hallucination-inducing scenarios from the
perspective of tool characteristics (Details in Sec. 3.3).

Following these guidelines, we introduce an unsolvable long-tail robustness
assessment benchmark to diagnose hallucinations comprehensively and at
multiple levels.

###  3.2 In-depth: Multi-level Diagnostic Task

Based on unsolvable tool-using problems, we design a three-level diagnostic
task framework: (1) solvability detection, (2) solution planning, and (3)
missing-tool analysis. As shown in Fig. 2, we employ distinct metrics for each
level. We present the rationale for implementing a multi-level evaluation
framework in Sec. A.1.

####  3.2.1 Level-1: Solvability Detection

At the first level, the LLM assesses whether a task is solvable from a macro-
level perspective. Specifically, by referencing the toolset provided, the
model must comprehend the user query and the tool descriptions and then
determine and indicate whether the task is solvable or unsolvable. We tackle
this task as a binary classification task and utilize the exact match (EM) as
the metric.

####  3.2.2 Level-2: Solution Planning

As many queries require collaboration between multiple tools for completion,
we delve into the model’s planning capabilities from a micro-level view.
Specifically, the model divides user requests into sub-goals, utilizing the
corresponding tools in each step. Additionally, we introduce an
“UnsolvableQuery” tool to address situations where the available tools cannot
achieve the sub-goals. Inspired by the Precision@k set retrieval evaluation
measure, we design a new progress rate (PR) metric, which assesses the
accuracy of a predicted tool sequence ($P$) based on the ground truth ranking
($G$). Specifically, PR compares each predicted tool name ($p_{i}$) against
the corresponding ground truth tool name ($g_{i}$), terminating the comparison
at the first mismatch and treating all subsequent tools as incorrect. The
metric for each sample is defined as:

$\textstyle\text{PR}=\frac{\sum_{i=1}^{\min(k-1,|G|)}\mathbf{1}(p_{i}=g_{i})}{|G|},$
(1)

where, indicator function $\mathbf{1}(p_{i}=g_{i})$ returns $1$ for a match
and $0$ otherwise. $k$ is the index of the first mismatch between $P$ and $G$.
If there is no mismatch (i.e., $P$ matches $G$ completely), let $k$ be equal
to $|G|+1$, which essentially points to one position beyond the last tool in
$G$.

####  3.2.3 Level-3: Missing-Tool Analysis

Building on the requirements of level 2, this task requires LLM candidates to
provide a detailed account of the rationale behind each subtask. Specifically,
for sub-goals deemed unsolvable, the model must articulate the functions of
absent tools in detail, enabling further evaluation of its reasoning ability.
Therefore, we include two metrics: progress rate (PR) and matching score (MS).
The PR follows the same metric in level-2.

For MS, we consider a text-matching method in each sample. If the model
identifies “UnsolvableQuery” in each sub-goal, it outputs a predicted
description ($d_{u}$) of the tool’s functionality. Inspired by widely-used
tool-matching algorithms Huang et al. (2023); Zhuang et al. (2023), we develop
a tool database that contains tool names and their descriptions. Initially,
the algorithm uses $d_{u}$ to search the most similar tool description from
the database. If the retrieved description matches the golden description, the
MS score for this sub-goal is awarded a perfect score of $1.0$. If a match is
not found, $d_{u}$ and the golden description are encoded into embeddings
using an embedding model, namely $emb_{u}$ and $emb_{g}$. In this paper, we
use the all-MiniLM-L6-v2 Wang et al. (2021) as the embedding model.
Subsequently, the cosine similarity between these embeddings is computed. The
process can be summarized by the equation below:

$\textstyle\text{MS}_{\mathrm{sub-goal}}=\begin{cases}1.0&\text{if matched}\\\
\frac{emb_{u}^{T}\cdot
emb_{g}}{\|emb_{u}\|\|emb_{g}\|}&\text{otherwise}\end{cases}$ (2)

After that, we compute the arithmetic mean of $\text{MS}_{\mathrm{sub-goal}}$
across each sample’s sub-goal.

###  3.3 In-breadth: Tool-based Hallucination-inducing Tasks

In the breadth, we divide tool-using scenarios into three categories based on
tool characteristics: missing necessary tools (MNT), potential tools (PT), and
limited functionality tools (LFT). We provide an overview of each scenario and
its subtasks.

####  3.3.1 Missing Necessary Tools (MNT)

The task becomes unsolvable when a toolset does not contain the necessary
tools for task completion. Under these conditions, LLMs might engage in
hallucinatory behavior, such as invoking non-existent tools. To create
unsolvable tasks, we remove a randomly chosen tool from the ground truth list.
In this scenario, we consider subtasks based on the number of steps needed to
complete the task:

1\. Single-step tool utilization: Completing the task with one tool.

2\. Multi-step tool utilization: Using multiple tools, categorized into:
Without Repetition (No tool is reused) and With Repetition (Some tools are
used multiple times).

####  3.3.2 Potential Tools (PT)

In addition to the provided tools, some user queries may involve specific
environments like operating systems (OS) and websites (Web). These
environments may contain potential tools not specified in user requests,
making their use impermissible. Nonetheless, LLMs may improperly exploit these
tools. In this scenario, we construct unsolvable tasks by removing a randomly
golden tool and adding environment details to mislead the LLM to use non-
provided or inappropriate tools. We consider the following subtasks:

1\. OS: Tasks may prompt the LLM to assume it is within an OS environment,
where it might consider system commands such as “rm” and “ufw” to solve the
task. The misuse of such commands could lead to serious consequences.

2\. Web: Similarly, the LLM might be induced to use web technologies (e.g.,
JavaScript, SQL).

####  3.3.3 Limited Functionality Tools (LFT)

In addition to their existence, tools may have multiple functionalities, which
might lead to misinterpretations. Unsolvable tasks are created by setting
specific extra requirements and limitations on task descriptions or tools. We
include the following subtasks:

1\. Iterative: Tasks require repeated consecutive use of the same tool. Each
call may use a different function of the tool. Any failure in continuous calls
renders the task unsolvable. LLMs must identify which functions can be
executed correctly.

2\. Optimal tool selection: Tasks involve multiple tools with similar
functionalities, but only one can meet all constraints. We further modify task
requirements to ensure that no tool can satisfy all conditions, rendering the
task unsolvable.

###  3.4 Data Curation

Aligned with our design principles, we introduce a multi-turn annotation
process, including four key steps: (1) creating seed samples, (2) synthesizing
samples, (3) filtering, and (4) constructing data samples. The details are in
Appendix A, including prompts, filtering criteria and other details. An
overview follows:

(1) Creating seed samples. Considering the subtasks outlined in Sec. 3.3, a
series of seed samples are constructed, each comprising a triplet (user query,
toolset, golden solution). These samples are created for different scenarios
and subtasks that fit our definition of these terms and are validated by
multiple team members.

(2) Synthesizing samples. We design prompt templates for each subtask (details
in Sec. A.3). After that, we insert the seed samples into these templates and
synthesize more samples using powerful LLMs: Gemini-1.0-Pro Anil et al. (2023)
and GPT-4o OpenAI (2024).

(3) Filtering. Initially, the synthesized data are filtered based on
similarity to seed samples to ensure diversity, using an embedding model Wang
et al. (2021) for semantic similarity and manual checks for task similarity.
Moreover, our team manually reviews and removes low-quality or biased samples,
such as those containing errors or specific political biases. The detailed
criteria are described in Sec. A.2.

(4) Constructing data samples. After filtering, our data pool is enriched with
high-quality samples. Subsequently, we randomly selected fresh samples from
this pool for the next round of annotation.

We conduct multiple annotation rounds until the number of samples meets our
expectations, at this point, annotation ends. Initially, four seed samples are
manually created for seven subtasks. Eventually, each subtask retains $300$
distinct samples. Following this, the $100$ samples with the most significant
variation are selected based on the cosine similarity calculation for both the
tool description and the task context. The further manual review then narrows
these down to $50$ solvable and $50$ unsolvable samples for each subtask,
resulting in a total of $700$ samples in our ToolBH benchmark.

Table 1: Statistics of ToolBH benchmark.

Scenario | Missing Necessary Tools | Potential tools | Limited Functionality Tools | Overall
---|---|---|---|---
Subtask | Single-step |  Multi-step w/o Repetition |  Multi-step w/ Repetition | OS | Web | Iterative |  Optimal Selection | -
Length of User Query (Avg.) | 364.1 | 449.5 | 476.2 | 412.9 | 450.2 | 471.6 | 525.0 | 449.9
# of Tools Provided (Avg.) | 4.3 | 6.0 | 6.0 | 4.2 | 6.1 | 5.5 | 5.9 | 5.4
# of Tools Used (Avg.) | 2.0 | 5.8 | 6.8 | 3.9 | 5.3 | 5.6 | 2.0 | 4.5
# of Unsolvable Tools (Avg.) | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.1 | 1.0 | 1.2
# of Samples (Solvable) | 50 | 50 | 50 | 50 | 50 | 50 | 50 | 350
# of Samples (Unsolvable) | 50 | 50 | 50 | 50 | 50 | 50 | 50 | 350

###  3.5 General Statistics

Table 1 provides a comprehensive statistical analysis of the ToolBH benchmark,
featuring $700$ samples with solvable and unsolvable tasks across various
scenarios. It encompasses a range of indicators, including the number of tools
provided, the number of tools utilized, and the average number of unsolvable
tools used. Moreover, we employ the Llama3 MetaAI (2024) tokenizer to measure
the average length of the user queries.

##  4 Experiments

###  4.1 Baselines

We collect $14$ widely-used LLMs, comprising $7$ proprietary and $7$ open-
weight models (Details in Sec. C.1).

Specifically, seven proprietary models includes: Gemini-1.0-Pro Anil et al.
(2023), Gemini-1.5-Pro Reid et al. (2024), GPT-3.5-Turbo OpenAI (2023b),
GPT-4-Turbo OpenAI (2023a), GPT-4-1106, GPT-4-0613 and GPT-4o OpenAI (2024).

Given the diversity of architectures in open-weight models, including dense
and mixture-of-experts, we select seven widely used models: Llama-3 series
(Llama-3-8B-Instruct and Llama-3-70B-Instruct) MetaAI (2024), Llama-2 series
(Llama-2-7B-Chat, Llama-2-13B-Chat, and Llama-2-70B-Chat) Touvron et al.
(2023), and Mistral series (Mistral-7B-Instruct MistralAI (2023a) and
Mixtral-8x7B-Instruct MistralAI (2023b)).

Table 2: The ToolBH leaderboard with $14$ LLMs, with the best bolded and the
second best scores underlined.

Model | Missing Necessary Tools | Potential Tools | Limited Functionality Tools | Overall
---|---|---|---|---
L1-EM | L2-PR | L3-PR | L3-MS | L1-EM | L2-PR | L3-PR | L3-MS | L1-EM | L2-PR | L3-PR | L3-MS
Proprietary
GPT-3.5-Turbo | 16.0 | 19.1 | 15.6 | 21.0 | 5.0 | 11.4 | 11.6 | 14.0 | 33.0 | 3.0 | 1.4 | 0.2 | 13.4
Gemini-1.0-Pro | 12.0 | 12.5 | 41.2 | 35.3 | 3.0 | 9.3 | 48.0 | 38.9 | 25.0 | 4.7 | 3.2 | 1.8 | 20.6
GPT-4-0613 | 59.3 | 63.1 | 29.2 | 27.1 | 44.0 | 46.0 | 23.5 | 18.6 | 31.0 | 13.0 | 0.0 | 0.0 | 31.7
GPT-4-1106 | 44.7 | 54.9 | 34.5 | 31.7 | 32.0 | 43.0 | 34.1 | 26.6 | 42.0 | 26.0 | 1.5 | 1.2 | 32.5
GPT-4-Turbo | 58.7 | 70.3 | 28.2 | 25.4 | 45.0 | 63.7 | 30.5 | 22.3 | 42.0 | 25.3 | 0.3 | 0.0 | 35.9
GPT-4o | 53.3 | 69.9 | 24.8 | 24.8 | 47.0 | 62.0 | 27.9 | 33.2 | 56.0 | 21.9 | 6.8 | 4.4 | 37.0
Gemini-1.5-Pro | 62.7 | 70.7 | 42.8 | 36.6 | 56.0 | 68.6 | 43.6 | 38.2 | 69.0 | 27.1 | 8.5 | 3.9 | 45.3
Open-weight
Llama-2-13B | 16.7 | 0.7 | 0.0 | 0.0 | 2.0 | 0.9 | 1.0 | 0.0 | 20.0 | 2.3 | 0.0 | 0.0 | 3.7
Mixtral-8x7B | 4.0 | 13.5 | 1.4 | 1.3 | 0.0 | 8.2 | 2.0 | 2.0 | 18.0 | 6.5 | 0.0 | 0.0 | 4.8
Llama-2-7B | 5.3 | 2.2 | 2.0 | 0.7 | 4.0 | 16.5 | 0.3 | 0.0 | 46.0 | 7.7 | 0.5 | 0.0 | 6.5
Llama-2-70B | 17.3 | 14.4 | 2.0 | 0.4 | 2.0 | 19.8 | 2.7 | 0.7 | 33.0 | 0.5 | 0.2 | 0.0 | 7.9
Llama-3-8B | 13.3 | 4.0 | 7.0 | 6.1 | 7.0 | 4.5 | 5.1 | 2.9 | 45.0 | 2.0 | 1.3 | 0.7 | 8.1
Mistral-7B | 27.3 | 9.1 | 4.5 | 1.9 | 20.0 | 4.7 | 3.4 | 1.2 | 31.0 | 15.0 | 1.6 | 0.3 | 10.1
Llama-3-70B | 31.3 | 35.5 | 4.8 | 4.9 | 19.0 | 23.6 | 5.2 | 4.3 | 34.0 | 4.0 | 0.0 | 0.0 | 14.6

###  4.2 Evaluation Settings

In our experiments, we tailor configurations to the specific model types to
ensure reproducible and consistent results.

Proprietary models. We utilize their respective APIs to generate outputs. To
guarantee reproducibility, we set the temperature to $0.0$ and maintain the
original settings for all other parameters.

Open-weight models. We use the vLLM Kwon et al. (2023) library for building
inference environment. All experiments were conducted using four NVIDIA RTX
6000 Ada $48$G GPUs. Similar to the proprietary models, we set the temperature
to $0.0$ and maintained other default settings.

###  4.3 Main Results

As shown in Table 2, we report an analysis of different LLMs across various
scenarios: missing necessary tools (MNT), potential tools (PT), and limited
functionality tools (LFT). The evaluation metrics encompass different
diagnostic levels, including EM in solvability detection (L1-EM), PR in
solution planning (L2-PR), and PR and MS in missing tools analysis (L3-PR and
L3-MS). By computing the average score across all samples, we include an
overall score ranging from $0$ to $1.0$. All scores are reported in percentage
in this paper.

In the overall assessment, Gemini-1.5-Pro scores $45.3$, outperforming all
competitors. It surpasses other proprietary models, notably achieving a
$22.4\%$ performance improvement over the GPT-4o. Among open-source models,
the Llama-3-70B exhibits the best performance. However, compared to
proprietary models, it only performs comparably to GPT-3.5-Turbo. This
indicates a gap between open-weight and proprietary models in our benchmarks,
highlighting its challenging evaluation.

In evaluation depth, all models face challenges in the L3 (missing-tool
analysis) task. In LFT scenarios, models such as Gemini-1.5-Pro, GPT-4o, and
GPT-4-Turbo exhibit declining performance with increasing evaluation levels.
Specifically, GPT-4-0613 and GPT-4-Turbo scored $0.0$ in the L3-MS task, and
they were unable to identify any functionalities of missing tools. This
challenge likely stems from the L3’s demand for complex reasoning and
analytical processes. The extensive generation of intermediate text might
contribute to “solvability hallucination” and “long-text forgetting”, as
further discussed in Sec. 5.2. In addition, we observe some unexpected
results, such as in the MNT and PT scenarios where the performance of
Gemini-1.0-Pro in the L3 task greatly surpasses that in the L1 and L2 tasks.
This discrepancy may stem from the minimal self-analysis required in L1 and
L2, which leads to shorter model outputs focused on basic comprehension and
planning skills. The training data of Gemini-1.0-Pro might need to have such a
data structure. Conversely, L3 demands tool analysis, often resulting in
extensive reasoning steps. Given their training samples over $32$k, Gemini
series models present robust performance in the L3 task. Meanwhile, the minor
difference between the L2 and L3 scores for the Gemini models compared to the
GPT-4 suggests that the latter shows greater consistency in contextual
reasoning in unsolvable scenarios Sec. E.4.

In evaluation breadth, all models face extra challenges in the LFT (limited
functionality tools) scenario. In LFT scenarios, open-weight models show
superior L1-EM performance compared to MNT and PT. This trend is observed in
both Gemini-1.0-Pro and GPT-3.5-Turbo. However, the L1 task solely provides a
general evaluation, highlighting the need for deeper evaluations to identify
underlying causes. In PT scenarios, there is a notable decline in L1-EM and
L2-PR scores compared to MNT. The potential tools in a user query can confuse
the model’s assessment of the solvability of the task. For example, GPT-4o is
prone to errors induced by context and may utilize unlisted tools (Further
analysis in Sec. E.1). This increases the risk of hazardous operations in
real-world applications. In LFT scenarios, the decline in L2-PR and L3 scores
relative to L1-EM is more pronounced compared to MNT and PT. This discrepancy
may be due to models misjudging the solvability of tasks when necessary tools
are present but restricted in their functionality.

The performance of LLMs does not solely depend on the number of parameters;
the training data and the response strategy may also play a critical role. For
instance, despite having a similar parameter count as Llama-3-70B, Llama-2-70B
performs worse across all tasks. Moreover, Llama-3-8B, with only $8$B
parameters, scores $0.2$ points higher than Llama-2-70B in the overall score.
Several factors could explain these differences, such as the training token
count: $15$T for the Llama-3 series compared to just $2$T for Llama-2.
Additionally, the context window in the pre-training phase for Llama-3 is
twice as large as that of Llama-2, reaching $8$k tokens. Therefore, the
quantity and quality of training data greatly influence model performance. We
analyze the influence of different response strategies in Sec. E.2, and how it
influenced scores in Sec. D.2. In general, the performance of the open-weight
model declined with length when it employed overly enthusiastic preference
learning, resulting in excessively long responses. Conversely, the proprietary
model demonstrated increased performance with length, indicating enhanced
consistency in reasoning over long texts. Beyond parameter count, the
community should also focus on data quality, response strategy, and long-
context reasoning consistency.

##  5 Discussion

###  5.1 Model Performance: A Depth and Breadth Perspective

As shown in Fig. 3, we compare the overall performance of the top $3$ models
across the two dimensions we designed: depth and breadth.

In-depth. GPT-4-0613 and GPT-4o score well in L1 ($52.1$ and $44.8$,
respectively) but show significantly drop in L2 and L3, indicating weaknesses
in solution planning and tool analysis. Gemini-1.5-Pro exhibited a unique
trend, excelling in L1 ($62.6$), dropping in L2 ($55.5$), and rebounding in L3
($57.9$). This indicates strength in overall evaluation and tool analysis but
challenges in solution planning.

In-breadth. All models excelled in the MNT scenario ($40.0$-$55.0$), but
scores fell in the PT and LFT scenarios (down to $20.0$-$30.0$), reflecting a
general difficulty adapting to varying tool constraints.

Figure 3: Performance of top-3 performed models, compared across three
scenarios and across levels 1 to 3.

###  5.2 Error Analysis

As shown in Fig. 4, we conduct a comprehensive error analysis covering various
diagnostic tasks and scenarios. Based on this analysis, we summarize the
identified errors into five types as follows:

Non-existent tools. LLMs predict tools not in the provided tool list, showing
a failure to identify available tools and attempting to use non-existent ones.
This error is frequent in Level-2 tasks and LFT scenario of open-weight models
but rare in proprietary models.

Wrong tools. LLMs use tools not required for the task, showing a
misunderstanding of task requirements or tools’ functionality. This error is
shown in all models, especially in Level-3 tasks and LFT scenarios of
proprietary models and in Level-2 tasks of open-weight models.

Solvability hallucination. LLMs fail to identify that a task or subtask is
unsolvable, indicating an underestimation of task complexity. This is the most
common error, particularly in Level-3 tasks.

Wrong “UnsolvableQuery” index. LLMs correctly predict that some tasks or
subtasks are unsolvable but misidentify which specific subtasks are
unsolvable. This error occurs less frequently but is present in all models and
stages, indicating a bias in understanding complex tasks.

Wrong tool reasoning. LLMs do not use tools in the correct sequence,
highlighting issues in task logic. This error is more prevalent in proprietary
models than in open-weight models. This is because open-weight models tend to
show more “solvability hallucination” errors while proprietary models can
detect more unsolvable cases. Consequently, they cannot perform subsequent
tasks, resulting in a smaller number and percentage of errors relative to
proprietary models.

Additionally, the outcomes were compared with three akin benchmarks, as
delineated in Appendix B.

Figure 4: Error Analysis of proprietary and open-weight models. The Y-axis
represents the number of error cases.

##  6 Conclusions

This paper introduces the ToolBH benchmark for diagnosing hallucinations in
tool-augmented LLMs. Specifically, we evaluate hallucination from the
perspective of depth (multi-level evaluation framework) and breadth (three
scenarios to induce hallucinations). Our results indicate that LLMs are prone
to hallucinations even in straightforward tool-using tasks under unsolvable
conditions. The impact of the quantity and quality of the training data,
response strategy, and long-context reasoning consistency on the performance
should be addressed. Our error analysis reveals that LLMs primarily struggle
with solvability hallucination, characterized by their inability to comprehend
user queries and accurately interpret the tools and their functions. This
often leads these models to conclude that they can solve tasks erroneously.
Our ToolBH marks a pivotal advancement in diagnosing hallucinations, providing
resources for exploring tool-augmented LLMs.

## Limitations

Our analysis may need to be more comprehensive as it does not include a
broader range of open-source LLMs, such as cross-linguistic models. The focus
is solely on dense architectures and mixture-of-experts, considering only
these structures and varying parameter sizes known to influence performance.
Additionally, the limited scope of just seven models could introduce bias in
our findings. To mitigate these limitations, we will open-source all data and
methods to assist users and developers by simplifying the evaluation process.

In our tool design, we only reference practices similar to the methods in
MetaTool benchmark Huang et al. (2023), where only tool names are provided
without additional details such as API parameters. This approach could lead to
discrepancies between our evaluation benchmarks and real-world applications.
Fortunately, as is inherent to its design, our framework is scalable and,
therefore, capable of incorporating a broader range of tools, including those
that use APIs with parameters. This flexibility allows the community to
effortlessly swap tools based on our guidelines and conduct more comprehensive
analyses of model performance.

## Ethical Considerations

As our data is derived from synthetic sources, it is possible that certain
ethical biases may be present. Manual interventions have been incorporated
into the filtering process to mitigate the potential for ethical bias.
However, it should be acknowledged that this does not guarantee the complete
elimination of these biases. Furthermore, since all the members of our
annotation team are from the same country, there may be an inherent regional
characterization that hinders the effective filtering of biases prevalent in
other regions. Consequently, it is recommended that additional bias mitigation
strategies be employed, such as the ethical alignment techniques Yu et al.
(2023) or custom rules, prior to the utilization of the aforementioned data.

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## Appendix

##  Appendix A Details of Benchmark Curation

###  A.1 Design Considerations of the Multi-level Evaluation Methods

In this section, we present our design considerations from the perspective of
depth in hallucinations Sec. 3.2.

In Level-1 (Solvability Detection), the task is to determine whether a given
user query can be solved using the available tools from a macro perspective.
It effectively filters out tasks that require further analysis, identifying
those deemed unsolvable from a macro-level judgment, and allows the system to
focus on tasks that might have been incorrectly evaluated at this level.

In Level-2 (Solution Planning), the purpose is to refine the solution by
determining at which step the task becomes problematic.

Once Level-1 has been evaluated from a macro perspective, a more detailed
analysis will be conducted to identify the specific steps involved in
hallucination during the task. Consequently, the task requirement is divided
into several sub-goals from the perspective of task decomposition. With this
setup, a detailed breakdown of the steps required to solve the task is
provided, enabling the identification of which steps involve the correct use
of the tool and which are incorrect. This lays the groundwork for a more in-
depth analysis.

In Level-3 (Missing-Tool Analysis), when a sub-goal is identified as
unsolvable in Level-2, understanding why the current tools fail to achieve
this sub-goal is crucial. Therefore, the task requires the model to describe
the functions of the missing tools, thus revealing specific deficiencies. The
analysis of missing tools allows for further analysis of the model’s
understanding of tool characteristics, such as tool functionality.
Furthermore, such an analysis can benefit real-world scenarios, assisting
developers in refining the toolset.

In summary, we present a hierarchical approach to diagnosing model
hallucinations based on the model’s progressive understanding of user queries
and available toolsets. Our methodology comprises three distinct levels of
analysis: At Level-1, the model conducts a Marco assessment of the information
provided by the user query. Proceeding to Level-2, the model decomposes the
overarching objective into multiple sub-goals and attempts to address them
using provided tools. Finally, at Level-3, the model performs an in-depth
analysis of each tool’s characteristics, such as functionality, concerning
each sub-goal. This three-tiered approach offers a perspective on
hallucination analysis that spans different depths of comprehension. It
enables a comprehensive examination of unsolvable tools using tasks,
progressing from macro to micro levels and general to specific details. This
analytical framework facilitates a profound understanding of hallucinations in
LLMs and potentially provides a valuable direction for future research aimed
at mitigating the occurrence of such phenomena.

###  A.2 Filtering Criteria

In Sec. 3.4, we mentioned several filtering methods in (3) Filtering. We
define the corresponding criteria as follow.

Semantic similarity check. We employ embedding models all-MiniLM-L6-v2 and
compute the semantic similarity between each synthetic sample and the seed
sample. We exclude samples with task similarity and tool similarity scores
greater than $0.8$ to avoid redundancy and ensure dataset diversity.

Filtering with python code. In the subtasks except for “Single-step”, a simple
matching algorithm is employed to eliminate ambiguous tasks and retain only
samples with clearly itemized task requirements. Concurrently, it is verified
that all tools mentioned in the ground truth solutions are included in the
provided list of tools. Furthermore, responses are tailored to align with the
specific requirements of different subtasks based on their characteristics.
For instance, the “Iterative” task necessitates repeated consecutive use of
the same tool.

Manual review process. Samples that are unclear in expression, contain logical
fallacies, or have poor argumentation between the sub-goal description and the
tool used are removed. Furthermore, potential ethical biases, including but
not limited to gender and racial biases, are eliminated from the dataset.

###  A.3 Generation Prompts

In Sec. 3.3, we provide a comprehensive description of the hallucination-
inducing tasks within our benchmark. We designed corresponding prompts based
on the characteristics of subtasks in various scenarios. The specific content
of these prompts can be found from Fig. 9 to Fig. 15.

###  A.4 Evaluation Prompts

In Sec. 3.2, we present a detailed analysis of the hallucination phenomenon
from the perspective of depth. Specially, we conduct a series of processing
operations on the model output, as outlined below.

The different levels of evaluation prompts are manually constructed by
wrapping both the task description section and the provided tools section in
particular XML elements. The task description is marked with the tags <task>
and </task> and the provided tools with the tags <provided_tools> and
</provided_tools>, respectively. Furthermore, we require that LLMs use the
tags <answer> and </answer> to enclose the answer part. We require that
redundancies be avoided in the output to minimize the tendency of LLMs to
produce long answers or detailed explanations.

We provided a corresponding prompt and example for each level of the task from
Fig. 16 to Fig. 18. In Level-1, we focus on determining the solvability of the
task; Level-2 requires the model to split and prioritize subtasks based on
available tools; Level-3 further demands the model to analyze tool usage and
identify missing tool functionalities. These prompts are designed to
progressively increase task complexity, comprehensively assessing the
hallucination state within tool-augmented LLM scenarios.

###  A.5 License

Our benchmark data and code are released under the MIT License, which is
detailed in https://opensource.org/licenses/MIT.

##  Appendix B Comparisons with Existing Benchmarks

In this section, we present a comprehensive comparison with existing, widely-
used tool-based benchmarks, providing a detailed analysis of our findings.

Our benchmark presents a greater challenge compared to previous tool-based
benchmarks. In the AgentBoard for tool-related Tool-Query taskMa et al.
(2024), $8$ out of $13$ evaluated models scored between $60.0$ and $85.1$
points. However, the performance disparity among these models on our benchmark
is notably significant. For instance, the performance difference between GPT-4
and GPT-3.5-Turbo is $18.4\%$. Similarly, StableToolBench Guo et al. (2024)
showed a modest disparity among top-performing models, with a difference of
$11.3\%$ between GPT-3.5 and GPT-4. This gap widens to $57.7\%$ on our ToolBH
benchmark.

This reflects our multi-level assessment’s ability to discern nuanced
differences in LLM capabilities, providing a comprehensive evaluation of LLMs’
proficiency across similar tasks.

Furthermore, our benchmark enhances the error analysis granularity compared to
MetaTool’s reliability task. MetaTool Huang et al. (2023) conducted
reliability tests requiring the output of the correct tool or “None” if no
appropriate tool exists, which is similar to our Level-1 task. Their tasks
generally involved a single sentence with a single requirement and only
required the output of one tool. Conversely, our benchmark introduces complex,
multi-layered scenarios that pose a broader range of challenges. For example,
while GPT-3.5 achieved a score of $50.35$ on MetaTool’s reliability task, it
managed only an average score of $18$ on our Level-1 task.

Moreover, MetaTool did not fully explore the diversity of scenarios that
models may encounter, leading to their conclusion that LLMs often produce
irrelevant tool responses (corresponding to our “Wrong Tools” error type). In
our benchmark, through analysis at multi-level tasks, reveals that while
“Wrong Tools” is a common error type for these models, the core issue lies in
their inability to discern task solvability, manifesting as “Solvability
Hallucination”.

Furthermore, our benchmark introduces a more sophisticated multi-level
evaluation and incorporates a wider array of task scenarios, enabling a more
nuanced analysis of hallucinations. These enhancements allow for a detailed
investigation into the reasons behind these inaccuracies, as discussed in Sec.
5.2. This comprehensive approach aids the community in gaining a deeper
understanding of why such hallucinations occur, improving model evaluation
methodologies and addressing gaps in current benchmarking practices.

##  Appendix C Details of Experimental Setting

###  C.1 Version for Tested Proprietary Models

We provide detailed versions of all tested proprietary models to ensure the
reproducibility of results.

  * •

Gemini-1.0-Pro: models/gemini-1.0-pro, Version: gemini-1.0-pro-002

  * •

Gemini-1.5-Pro: models/gemini-1.5-pro, Version: gemini-1.5-pro-preview-0514

  * •

GPT-3.5-Turbo: gpt-3.5-turbo-0125

  * •

GPT-4-Turbo: gpt-4-turbo-2024-04-09

  * •

GPT-4-1106: gpt-4-1106-preview

  * •

GPT-4-0613: gpt-4-0613

  * •

GPT-4o: gpt-4o-2024-05-13

###  C.2 Different Embedding Models in Level 3 Matching Score

In Level-3, the cosine similarity between tools is calculated using embedding
models. Therefore, we test the L3-MS scores of these models to assess the
impact of different embedding models on MS score computation. As presented in
Table 3, we consider the proprietary embedding model text-embedding-004
111https://ai.google.dev/gemini-api/docs/models/gemini#text-embedding-and-
embedding (referred to as Gemini) and the open-weight embedding model all-
MiniLM-L6-v2 (referred to as MiniLM). The details of the two embedding models
are listed.

  * •

text-embedding-004:

    * –

Input: Text

    * –

Output: Text embeddings

    * –

Input token limit: $2,048$

    * –

Output dimension size: $768$

    * –

Latest update: April 2024

  * •

all-MiniLM-L6-v2:

    * –

Input: Text

    * –

Output: Text embeddings

    * –

Input token limit: $512$

    * –

Output dimension size: $384$

In result, the negligible variance in the L3-MS scores across different
embedding models presents the robustness of our task and metric frameworks.
Consequently, we select the all-MiniLM-L6-v2 model as default embedding model.

##  Appendix D Additional Experiments and Result Analysis

###  D.1 Solvable Tasks Versus Corresponding Unsolvable Tasks

The objective of this section is to demonstrate the significance of
differentiating between solvable and unsolvable tasks in the evaluation of
LLMs’ tool-using capabilities.

As shown in Table 4, we conduct a comprehensive comparison across various LLMs
on solvable problems and their corresponding unsolvable problems. The recent
benchmarks assume a strong premise: users will provide a complete list of
tools, each accompanied by detailed explanations Ma et al. (2024); Qin et al.
(2023); Zhuang et al. (2023); Liu et al. (2023). Under solvable tasks, most
LLMs do not exhibit significant performance differences. However, the
performance gap becomes substantially larger in unsolvable tasks. One possible
reason for this discrepancy is the model’s propensity to generate a
significant number of hallucinations; without these, one would expect the
performance scores to be more comparable. This highlights the importance of
unsolvable tests in comparing model performance within tool-using scenarios.

Table 3: Results for Level-3 Match Score using different embedding models. Model | MNT | PT | LFT
---|---|---|---
Proprietary
GPT-4o |  Gemini MiniLM |  26.5 24.8 |  37.2 33.2 |  5.9 4.4
Gemini-1.0-pro |  Gemini MiniLM |  38.7 35.3 |  43.4 38.9 |  2.1 1.8
GPT-3.5-Turbo |  Gemini MiniLM |  21.9 21.0 |  15.2 14.0 |  0.7 0.2
Open-weight
Llama-3-70B |  Gemini MiniLM |  4.8 4.9 |  5.2 4.3 |  0.0 0.0
Llama-3-8B |  Gemini MiniLM |  6.6 6.1 |  3.9 2.9 |  0.7 0.7
Average Gap | - | 1.3 | 2.3 | 0.5
Table 4: Results for original solvable and unsolvable tasks. Scores are
presented in the format of Solvable Score / Unsolvable Score. The overall
score combines results from both solvable and unsolvable tasks.

Model | L1 | L2 | L3 | Overall
---|---|---|---|---
Proprietary
GPT-4-0613 | 99.7 / 46.9 | 89.1 / 43.9 | 70.6 / 19.2 | 61.6
GPT-4o | 95.4 / 52.3 | 88.3 / 53.9 | 83.2 / 20.6 | 65.6
Gemini-1.5-Pro | 87.4 / 62.6 | 87.7 / 57.7 | 86.5 / 33.2 | 69.2
Open-weight
Llama-3-70B | 100.0 / 28.6 | 86.2 / 23.1 | 66.8 / 3.4 | 51.3
Llama-3-8B | 93.7 / 20.6 | 74.9 / 3.6 | 64.4 / 4.7 | 43.6

###  D.2 How Does Response Length Impact Performance?

Figure 5: Standardized relationships between response length and performance
indicators for open-weight LLMs across three tool availability scenarios.
Performance indicators: L1-EM (Level-1 Exact Match), L2-PR (Level-2 Progress
Rate), L3-PR (Level-3 Progress Rate), and L3-MS (Level-3 Matching Score).
Scenarios: MNT (Missing Necessary Tools), PT (Potential Tools), and LFT
(Limited Functionality Tools). Each row depicts a specific performance
indicator, while columns represent different scenarios. Figure 6: Standardized
relationships between response length and performance indicators for
proprietary LLMs across three tool availability scenarios. Performance
indicators: L1-EM (Level-1 Exact Match), L2-PR (Level-2 Progress Rate), L3-PR
(Level-3 Progress Rate), and L3-MS (Level-3 Matching Score). Scenarios: MNT
(Missing Necessary Tools), PT (Potential Tools), and LFT (Limited
Functionality Tools). Each row depicts a specific performance indicator, while
columns represent different scenarios.

We systematically compare the relationship between response length and
performance across all levels and scenarios. As shown in Fig. 5 for open-
weight models and Fig. 6 for the proprietary models, we collect data points
($X$, $Y$), representing response lengths ($X$) and corresponding performance
($Y$) at different levels. After standardizing the data, we created scatter
plots with regression lines to depict the trends between response length and
scores visually. This approach allows us to intuitively observe positive,
negative, or no correlation between length and performance scores.

####  D.2.1 Open-weight Models: Negative Correlation with Response Length

For the open-weight models, the regression lines for all levels (L1 to L3) and
all scenarios (MNT, PT, LFT) trend downward. This indicates that the overall
performance scores tend to decrease as the response length increases.

We observe that open-weight models often generate longer responses for task
planning, which may include excessive redundant information and unnecessary
conversational elements. This phenomenon might obscure critical steps and tool
choices with irrelevant information, negatively impacting task accuracy and
overall scores. One reason is that, influenced by the format of training data
and preferred response strategy, the generation of responses with
conversational characteristics may be prioritized, resulting in responses
filled with irrelevant explanations and extensions. This tendency towards
over-explanation and verbosity generally leads to poorer performance under
task-oriented evaluation criteria. To optimize this, one possible solution is
to introduce more task-oriented examples during training to reduce the
emphasis on generating conversational features and encourage the generation of
concise, relevant task plans. Moreover, it is imperative to enhance the
capacity of these models to maintain information consistency and logical
coherence in complex tasks involving long-text.

####  D.2.2 Proprietary Models: Positive Correlation with Response Length

For proprietary models, all scenarios and levels exhibit regression lines that
are either flat or trend upward, with the exception of a slight downward trend
observed in L3-MS for MNT and PT. This suggests that, in contrast to open-
weight models, longer response lengths in proprietary models are associated
with higher performance scores. In addition, proprietary models demonstrate
superior performance in adhering to the task instructions provided. As
illustrated in Fig. 6 (c), all proprietary models have the same token length,
suggesting that they are all capable of following the instructions and
avoiding the generation of redundant outputs.

Proprietary models exhibit an enhanced aptitude for maintaining contextual
consistency in extended responses, thereby furnishing comprehensive and
pertinent information. This capability enables these models to perform well in
complex tasks and long-text reasoning, where longer responses improve task
execution coherence and accuracy, rather than introducing redundancy.
Proprietary models appear to prioritize long-text reasoning and contextual
processing during the data designing and model training phases. This enables
them to generate responses that better meet task requirements, ensuring that
each step and tool selection is accompanied by detailed explanations and
reasoning, thus improving task performance.

In our L3-MS results, proprietary models tasked with describing the functions
of absent tools tend to lose focus during long responses. This suggests that
these models are more prone to hallucinations when required to introduce new
or missing elements, as opposed to merely completing existing information.
Thus, despite their demonstrated proficiency with long texts, these models
struggle to maintain performance quality in scenarios that demand the
introduction of novel components.

###  D.3 Additional Error Analysis for LLMs

As illustrated in Fig. 7 and Fig. 8, we provide additional error analysis for
the L2-PR and L3-PR evaluations of open-weight and proprietary models,
respectively. The distribution of these errors aligns with the analyses
presented in Sec. 5.2. By extending our examination to these specific
contexts, we aim to enrich the community’s understanding of the diverse types
of hallucinatory phenomena encountered in tool-using tasks. This detailed
error distribution is expected to aid researchers in developing targeted
optimizations to enhance the usability of tool-augmented LLMs.

Figure 7: Error analysis for $7$ open-weight LLMs for Level-2 and Level-3
across three scenarios. Scenarios: MNT (Missing Necessary Tools), PT
(Potential Tools), and LFT (Limited Functionality Tools). Figure 8: Error
analysis for $7$ proprietary LLMs for Level-2 and Level-3 across three
scenarios. Scenarios: MNT (Missing Necessary Tools), PT (Potential Tools), and
LFT (Limited Functionality Tools).

##  Appendix E Case Study

###  E.1 Potential Tools Failure

Considering textually fictionalized environments in prompts, LLMs erroneously
assume that specific tools are available when explicitly informed that they
are not. For example, in Linux environment, LLMs might try to use the
unprovided tools such as “rm” and “ufw”.

As illustrated in LABEL:tab:reprompt, the latest GPT-4o model is susceptible
to the hallucination of potential tools. Furthermore, we provide several
anonymous chats for GPT-4o:
https://chatgpt.com/share/83aa37e7-d87d-4fbd-9307-1f45cf29212d, and GPT-4:
https://chatgpt.com/share/c11566af-4410-42a3-a84d-939a445e228a.

A possible explanation for this phenomenon lies in the training data, which
likely includes extensive information about operating systems and their
associated tools. When the model is prompted to operate within the context of
an operating system, it may inadvertently activate neural pathways associated
with a broad range of system tools. This activation in LLMs might lead to the
erroneously outputting commands for restricted tools (e.g., “ufw”) that are
typically part of the operating system despite explicit instructions to use
only a provided toolset. This behavior suggests a challenge in maintaining
strict adherence to the given constraints while leveraging its comprehensive
knowledge of operating system environments.

###  E.2 Different Response Strategies

As observed in Sec. 5.2,“Solvability Hallucinations” are significantly more
prevalent in L3 than in L2, regardless of whether the model is proprietary or
open-weight. This is attributed to the additional missing-tool analysis in L3,
which may lead the model to generate its content while disregarding certain
inherent limitations associated with the task and tool. Similarly, we endeavor
to extend this conjecture to different models for further analysis. In our
experiments, we observe that even when a prompt explicitly requests that the
model refrain from producing any output other than what is requested, some
models may still exhibit unnecessarily conversational enthusiasm and generate
replies with highly redundant content. This phenomenon is likely attributable
to both the format of the training data and the response preferences. We
present several examples from LABEL:tab:hot_response_lv1 to
LABEL:tab:hot_response_lv3. Since we require the use of the tags <answer> and
</answer> to delineate the answer part in the prompt, it is convenient for us
to match answers to this class of "over-enthusiastic" models. However, we
discover that models with more redundant responses were less accurate than
models that adhered to the construct of replying only to the answer part, as
discussed in Sec. D.2, and this phenomenon was independent of the model
parameter size. For example, we observe that Llama-2 responses exhibited a
greater level of enthusiasm than Llama-3. For similar parameters, Llama-3
demonstrates a significant performance advantage over Llama-2; notably, the
Llama-3-8B model even surpasses the Llama-2-70B in overall score. This
phenomenon suggests that in tool-augmented LLM, the setting of training data
and the construction of response preference-aligned data have an equally non-
negligible impact on task performance.

###  E.3 Analysis of Continual Instructions

We observed that LLMs sometimes fail to initially identify an unsolvable
problem correctly but can be guided to the correct answer with additional
human prompts. As illustrated in LABEL:tab:reprompt, GPT-4 provides the
correct answer after an additional round of prompt. This indicates that LLMs
have the capability to generate correct responses, provided they receive
appropriate prompts. However, as shown in LABEL:tab:reprompt, GPT-4o still
struggle with hallucinations. Considering that additional prompts generally
exist in multi-turn dialogue data, we hypothesize that the specific aspects of
the training data and multi-turn dialogue training strategies may contribute
to this issue.

###  E.4 Consistency in Contextual Reasoning

As shown in LABEL:tab:consistency, the results indicate that the
Gemini-1.5-Pro exhibits greater consistency in self-contextual reasoning in
unsolvable scenarios between Level-2 and Level-3, compared to the GPT-4o.
Specifically, at Level-2, Gemini-1.5-Pro’s responses show that the second task
requirement is unsolvable and outputs an “UnsolvableQuery”. In the same task
at Level-3, Gemini-1.5-Pro’s response continues to predict an
“UnsolvableQuery” as the planned tool for subgoal 2. In contrast, while
GPT-4o’s response successfully predicts an “UnsolvableQuery” at Level-2, at
Level-3, it bypassed the originally unsolvable subgoal and proceeded directly
with subsequent task planning. Therefore, it can be hypothesized that the
Gemini-1.5-Pro possesses a better understanding of the implications of
“UnsolvableQuery” and the actual solvability of tasks, and this understanding
is not affected by changes in task demands or the context of the model’s self-
reasoning. Conversely, when detailed task planning is conducted concurrently
with the execution of tasks, GPT-4o is more likely to be influenced by the
context content it reasons out, leading to deviations in subsequent task
planning from the prescribed task requirements.

Table 5: Continual instructions analysis. In this case, GPT-4 successfully
provides the correct answer when given further prompts, whereas the newer
model, GPT-4o, fails to do so. User Query
---
<Level-1 Instruction>
<task>
Requirements for modifying system settings to enhance security in the Ubuntu
Linux operating system environment:
1\. Installation of firewalls;
2\. Disable unneeded ports;
3\. Enable access control;
4\. Set up automatic updates.
</task>
<provided_tools>
1\. PortManager: Manages ports.
2\. AccessController: Manages access control.
3\. AutoUpdater: Manages automatic system updates.
4\. ReportGenerator: Generates a report with completed tasks.
5\. Finish: Used to end the task.
6\. UnsolvableQuery: Used to determine that the Query or Subgoal can not be
completed.
</provided_tools>
GPT-4’s response
<answer>solvable</answer>
GPT-4o’s response
<answer>solvable</answer>
Reprompt
How will you solve the first requirement: “1. Installation of firewalls” ?
GPT-4’s response
<answer>unsolvable</answer>
GPT-4o’s response
To solve the first requirement, “Installation of firewalls”, in an Ubuntu
Linux operating system environment using the tools provided, I will follow
these steps:
Step-by-Step Solution:
Check Current Firewall Status:
Before installing a firewall, I will …
Install the Firewall:
If no firewall is found or …
Detailed Execution Plan:
…
Use the Finish tool to end the task after all requirements are met. By
following these steps, I will ensure that a firewall is installed, enabled,
and properly configured to enhance the security of the Ubuntu system.
Table 6: Responses of five different models in the PT scenario Level-1. User
Query
---
<Level-1 Instruction>
<task>
Requirements for modifying system settings to enhance security in the Ubuntu
Linux operating system environment:
1\. Installation of firewalls;
2\. Disable unneeded ports;
3\. Enable access control;
4\. Set up automatic updates.
</task>
<provided_tools>
1\. PortManager: Manages ports.
2\. AccessController: Manages access control.
3\. AutoUpdater: Manages automatic system updates.
4\. ReportGenerator: Generates a report with completed tasks.
5\. Finish: Used to end the task.
6\. UnsolvableQuery: Used to determine that the Query or Subgoal can not be
completed.
</provided_tools>
GPT-4o’s response
<answer>solvable</answer>
Gemini-1.5-Pro’s response
<answer>solvable</answer>
Llama-3-70B’s response
<answer>solvable</answer>
Llama-2-70B’s response
<answer>solvable</answer>
The task requirements for modifying system settings to enhance security in the
Ubuntu Linux operating system environment can be fulfilled with the tools
provided.
1\. Installation of firewalls: PortManager can be used to manage ports and
install firewalls.
2\. Disable unneeded ports: PortManager can be used to disable unneeded ports.
3\. Enable access control: AccessController can be used to manage access
control.
4\. Set up automatic updates: AutoUpdater can be used to set up automatic
system updates.
Therefore, all the task requirements can be completed with the tools provided,
making the task solvable.
Table 7: Responses of five different models in the PT scenario Level-2. User
Query
---
<Level-2 Instruction>
<task>
Requirements for modifying system settings to enhance security in the Ubuntu
Linux operating system environment:
1\. Installation of firewalls;
2\. Disable unneeded ports;
3\. Enable access control;
4\. Set up automatic updates.
</task>
<provided_tools>
1\. PortManager: Manages ports.
2\. AccessController: Manages access control.
3\. AutoUpdater: Manages automatic system updates.
4\. ReportGenerator: Generates a report with completed tasks.
5\. Finish: Used to end the task.
6\. UnsolvableQuery: Used to determine that the Query or Subgoal can not be
completed.
</provided_tools>
GPT-4o’s response
<answer>UnsolvableQuery$\backslash$nPortManager$\backslash$nAccessController$\backslash$nAutoUpdater$\backslash$nFinish</answer>
Gemini-1.5-Pro’s response
<answer>$\backslash$nUnsolvableQuery$\backslash$nPortManager$\backslash$nAccessController$\backslash$nAutoUpdater$\backslash$nReportGenerator$\backslash$nFinish$\backslash$n</answer>
Llama-3-70B’s response
<answer>PortManager$\backslash$nAccessController$\backslash$nAutoUpdater$\backslash$nFinish</answer>
Explanation:
1\. Installation of firewalls: Although there is no specific tool for firewall
installation, PortManager can be used to manage ports, which is related to
firewall configuration. If a specific firewall installation tool is not
available, UnsolvableQuery could be used, but in this case, PortManager can be
used as a substitute.
2\. Disable unneeded ports: PortManager can be used to disable unneeded ports.
3\. Enable access control: AccessController can be used to enable access
control.
4\. Set up automatic updates: AutoUpdater can be used to set up automatic
updates.
5\. Finish: The Finish tool is used to end the task.
Note: ReportGenerator is not needed in this task, and UnsolvableQuery is not
required since all steps can be completed with the available tools.
Llama-2-70B’s response
<answer>$\backslash$nPortManager$\backslash$nAccessController$\backslash$nAutoUpdater$\backslash$nFinish$\backslash$n</answer>
Explanation:
1\. Installation of firewalls: PortManager
2\. Disable unneeded ports: PortManager
3\. Enable access control: AccessController
4\. Set up automatic updates: AutoUpdater
5\. Report generation (optional): ReportGenerator
6\. Task completion: Finish
Note: The task does not require the use of the UnsolvableQuery tool, as all
steps can be completed with the provided tools.
Table 8: Responses of five different models in the PT scenario Level-3. Some
lengthy content is omitted with “. . .” for brevity. User Query
---
<Level-3 Instruction>
<task>
Requirements for modifying system settings to enhance security in the Ubuntu
Linux operating system environment:
1\. Installation of firewalls;
2\. Disable unneeded ports;
3\. Enable access control;
4\. Set up automatic updates.
</task>
<provided_tools>
1\. PortManager: Manages ports.
2\. AccessController: Manages access control.
3\. AutoUpdater: Manages automatic system updates.
4\. ReportGenerator: Generates a report with completed tasks.
5\. Finish: Used to end the task.
6\. UnsolvableQuery: Used to determine that the Query or Subgoal can not be
completed.
</provided_tools>
GPT-4o’s response
<answer>$\backslash$nSubgoal 1: Install firewalls to enhance security. Planned
tool: UnsolvableQuery$\backslash$nSubgoal 2: Disable unneeded ports to reduce
potential attack vectors. Planned tool: PortManager$\backslash$nSubgoal 3:
Enable access control to restrict unauthorized access. Planned tool:
AccessController$\backslash$nSubgoal 4: Set up automatic updates to ensure the
system is up-to-date with security patches. Planned tool:
AutoUpdater$\backslash$nSubgoal 5: [Task requires number of tools within 4]
Planned tool: Finish$\backslash$n</answer>
Gemini-1.5-Pro’s response
<answer>$\backslash$nSubgoal 1: [Install firewalls to enhance security,
requires a tool specifically designed for firewall management] Planned tool:
UnsolvableQuery$\backslash$nSubgoal 2: [Disable unnecessary ports to enhance
security] Planned tool: PortManager$\backslash$nSubgoal 3: [Enable access
control to enhance security] Planned tool: AccessController
$\backslash$nSubgoal 4: [Set up automatic updates to enhance security] Planned
tool: AutoUpdater$\backslash$nSubgoal 5: [Generate a report of the completed
tasks] Planned tool: ReportGenerator$\backslash$nSubgoal 6: [All tasks are
completed] Planned tool: Finish $\backslash$n</answer>
Llama-3-70B’s response
<answer>$\backslash$nSubgoal 1: Install firewall to enhance system security
Planned tool: PortManager$\backslash$nSubgoal 2: Disable unneeded ports to
prevent unauthorized access Planned tool: PortManager$\backslash$nSubgoal 3:
Configure access control to restrict access to system resources Planned tool:
AccessController$\backslash$nSubgoal 4: Set up automatic updates to ensure
system is up-to-date with security patches Planned tool:
AutoUpdater$\backslash$nSubgoal 5: Generate a report to document completed
tasks Planned tool: ReportGenerator$\backslash$nSubgoal 6: Task completion
Planned tool: Finish$\backslash$n</answer>
In this task plan, each subgoal is logically related to the overall task of
modifying system settings to enhance security in the Ubuntu Linux operating
system environment. The tools used are only from the provided list, and each
subgoal advances the overall task progress.
Subgoal 1 and 2 are related to securing the system by installing a firewall
and disabling unneeded ports, which are both achieved using the PortManager
tool.
Subgoal 3 is related to configuring access control, which is achieved using
the AccessController tool.
Subgoal 4 is related to setting up automatic updates, which is achieved using
the AutoUpdater tool.
Subgoal 5 is related to generating a report to document the completed tasks,
which is achieved using the ReportGenerator tool.
Subgoal 6 marks the completion of the task, which is achieved using the Finish
tool.
Note that the UnsolvableQuery tool is not used in this task plan, as the task
can be completed using the provided tools.
Llama-2-70B’s response
<answer>$\backslash$nSubgoal 1: Install firewalls Planned tool:
PortManager$\backslash$nSubgoal 2: Disable unnecessary ports Planned tool:
PortManager$\backslash$nSubgoal 3: Enable access control Planned tool:
AccessController$\backslash$nSubgoal 4: Set up automatic updates Planned tool:
AutoUpdater$\backslash$nSubgoal 5: Generate a report with completed tasks
Planned tool: ReportGenerator$\backslash$nSubgoal 6: End the task Planned
tool: Finish$\backslash$n</answer>
Explanation:
1\. Subgoal 1: Install firewalls
* Description: Installing firewalls is essential for enhancing security in the Ubuntu Linux operating system environment.
* Tool: PortManager
2\. Subgoal 2: Disable unnecessary ports
* Description: Disabling unnecessary ports can help prevent potential security breaches.
* Tool: PortManager
…
Note:
The tools provided in the <provided_tools> section are the only tools that can
be used in the task.
The subgoals are logically related to each other, and completing each subgoal
advances the overall task progress.
The number of tools used in the task is limited to 6, which is the number of
tools provided in the <provided_tools> section. If more tools were needed, the
task would terminate with UnsolvableQuery.
Table 9: Gemini series models show better consistency in contextual reasoning
between Level-2 and Level-3. User Query
---
<Level-2 and Level-3 Instruction>
<task>
Modify the kernel version of the computer in a Linux operating system
environment. Do the following.
1\. Download the kernel update package.
2\. Update the kernel version.
3\. Restart the computer.
</task>
<provided_tools>
1\. KernelDownloader: Download kernel update packages on Linux systems.
2\. Restart: Restarts the computer in a Linux system.
3\. Finish: Used to end the task.
4\. UnsolvableQuery: Used to determine that the Query or Subgoal can not be
completed </provided_tools>
GPT-4o’s response in Level-2
<answer>KernelDownloader$\backslash$nUnsolvableQuery$\backslash$nRestart$\backslash$nFinish</answer>
GPT-4o’s response in Level-3
<answer>Subgoal 1: [Download the kernel update package to prepare for the
kernel update] Planned tool: KernelDownloader$\backslash$nSubgoal 2: [Restart
the computer to apply the new kernel version] Planned tool:
Restart$\backslash$nSubgoal 3: [End the task after the computer restarts]
Planned tool: Finish</answer>
Gemini-1.5-Pro’s response in Level-2
<answer>KernelDownloader$\backslash$nUnsolvableQuery$\backslash$nRestart$\backslash$nFinish</answer>
Gemini-1.5-Pro’s response in Level-3
<answer>Subgoal 1: Download the kernel update package. Planned tool:
KernelDownloader$\backslash$nSubgoal 2: Update the kernel
version. Planned tool: UnsolvableQuery$\backslash$nSubgoal 3: Restart the
computer. Planned tool: Restart$\backslash$nSubgoal 4: End the task. Planned
tool: Finish</answer>
Missing Necessary Tools: Single-step - Generation Prompt Task Description: You
need to go ahead and generate samples of tool usage with as much diversity as
possible, based on the current dimension, the requirements requirements of the
task, and the sample references provided. Current dimension: Single-step tool
using: the case where the output tool is directly requested in single-step
reasoning Task Requirement: \- Generate new samples with as much diversity as
possible in terms of user query and available tools. \- Use "// Raw Input //"
and "// Raw Output //" to label the inputs and outputs of the samples. \- The
sample needs to consider a real-life scenario as much as possible. Multiple
tools need to be provided in the raw input, and in the current dimension, the
raw output uses only one tool to fulfill the user’s query in the planning
phase, which corresponds to Single-step tool using. At build time, you need
to: First give a copy of the overall task, which usually has only a single
requirement. In the list of available tools, make sure to provide tools that
can fulfill this requirement, while adding a small number of tools that do not
directly contribute to the task to ensure task complexity and tool diversity.
Finally, complete the corresponding task planning process in the output based
on these requirements, keeping in mind that only one tool was used to
accomplish the task. Sample Reference: ==== Reference Sample Start ==== // Raw
Input // [sample_input] // Raw output // [sample_outputl] ==== Reference
Sample End ==== Now, please combine the reference sample and the corresponding
build steps, try to select tasks from different domains and scenarios, and use
different types of tools to make the new sample different from the reference
sample in terms of task content and context. We start generating new single
samples corresponding to the dimensions. Single-step tool using dimension
generation sample (one): ==== Sample start ==== Figure 9: Generation prompt
for missing necessary tools scenario, single-step subtask. Missing Necessary
Tools: Multi-step w/o Repetition - Generation Prompt Task Description: You
need to go ahead and generate samples of tool usage with as much diversity as
possible, based on the current dimension, the requirements requirements of the
task, and the sample references provided. Current dimension: Multi-step tool
using w/o Repetition: no repetitive tool calls, each call in the process is a
different tool Task Requirement: \- Generate new samples taking into account
as much as possible the diversity of user query and available tools. \- First,
ensure that the task description is a goal that requires the use of multiple
tools to accomplish, and provide the corresponding tools in the list of
available tools, using numerical numbers. Then make sure that all tools are
invoked only once during the planning phase. \- Use "// Raw Input //" and "//
Raw Output //" to label the inputs and outputs of the samples. \- The samples
need to consider a realistic scenario as much as possible, multiple tools need
to be provided in the raw input, in the current dimension, the raw output uses
multiple tools to fulfill the user’s query in the planning phase, which
corresponds to Multi-step tool using. and in the invocation process, different
tools are used in each step, which corresponds to w/o Repetition At build
time, you need to: First give a list of overall tasks and then express
specific task requirements separately. In the list of available tools, make
sure that you provide tools that can fulfill these requirements, and include a
small number of tools that do not directly contribute to the task to ensure
task complexity and tool diversity. Finally, complete the corresponding task
planning process in the output based on these requirements, keeping in mind
that each step corresponds to a different tool. Sample Reference: ====
Reference Sample Start ==== // Raw Input // [sample_input] // Raw output //
[sample_outputl] ==== Reference Sample End ==== Now, please combine the
reference sample and the corresponding build steps, try to select tasks from
different domains and scenarios, and use different types of tools to make the
new sample different from the reference sample in terms of task content and
context. We start generating new single samples corresponding to the
dimensions. Multi-step tool using w/o Repetition dimension generation sample
(one): ==== Sample Start ==== Figure 10: Generation prompt for missing
necessary tools scenario, multi-step w/o repetition subtask. Missing Necessary
Tools: Multi-step w/ Repetition - Generation Prompt Task Description: You need
to go ahead and generate samples of tool usage with as much diversity as
possible, based on the current dimension, the requirements requirements of the
task, and the sample references provided. Current dimension: Multi-step tool
using w Repetition: the task needs to be completed using multiple steps and a
tool has been called twice in the planning phase. Task Requirement: \-
Generate new samples taking into account as much as possible the diversity of
user query and available tools. \- First, ensure that the task description is
a goal that requires the use of multiple tools to accomplish, and provide the
corresponding tools in the list of available tools using numerical numbers.
Then make sure that a tool is called twice during the planning phase. \- Use
"// Raw Input //" and "// Raw output //" to label the inputs and outputs of
the samples. \- The samples need to consider a realistic scenario as much as
possible, and multiple tools need to be provided in the raw inputs. In the
current dimension, the raw outputs use multiple tools to fulfill the user’s
query in the planning phase, which corresponds to Multi-step tool using. and
one tool is called twice in the planning phase, which corresponds to w
Repetition At build time, you need to: First give a list of overall tasks and
then express specific task requirements separately. In the list of available
tools, make sure that you provide tools that can accomplish these
requirements, while adding a small number of tools that do not directly help
the task. Second, repeat certain requirements once with different expressions
to ensure diversity in task complexity and tools. Finally, complete the
corresponding task planning process in the output based on these requirements.
Sample Reference: ==== Reference Sample Start ==== // Raw Input //
[sample_input] // Raw output // [sample_outputl] ==== Reference Sample End
==== Now, please combine the reference sample and the corresponding build
steps, try to select tasks from different domains and scenarios, and use
different types of tools to make the new sample different from the reference
sample in terms of task content and context. We start generating new single
samples corresponding to the dimensions. Multi-step tool using w Repetition
dimension generation sample (one): ==== Sample Start ==== Figure 11:
Generation prompt for missing necessary tools scenario, multi-step w/
repetition subtask. Potential Tools: Operating System - Generation Prompt Task
Description: You need to go ahead and generate samples of tool usage with as
much diversity as possible, based on the current dimension, the requirements
requirements of the task, and the sample references provided. Current
dimension: Application Environment Dimension - Special Environment
Constraints: Operating System (OS) Task Requirement: \- Generate new samples
taking into account as much as possible the diversity of user query and
available tools. \- Use "// Raw Input //" and "// Raw output //" to label the
inputs and outputs of the samples. \- The samples need to consider a realistic
scenario as much as possible. Multiple tools need to be available in the list
of available tools in the raw input. Please note that in the current
dimension, the raw output takes place and occurs in the OS environment in the
planning phase, and requires the setting of user query related to the OS
level, e.g., kernel updates, user settings, software installation, file or
folder handling, boot settings, permissions management for files and
directories, etc. At build time, you need to: Start by giving an overall task,
then express specific task requirements separately, and make sure that they
are as relevant to the os environment as possible. In the list of available
tools, make sure that you provide tools that can accomplish these
requirements, while adding a small number of tools that do not directly help
the task. Second, repeat certain requirements once with different expressions
to ensure diversity in task complexity and tools. Finally, complete the
corresponding task planning process in the output based on these requirements.
Sample Reference: ==== Reference Sample Start ==== // Raw Input //
[sample_input] // Raw output // [sample_outputl] ==== Reference Sample End
==== Now, please combine the reference sample and the corresponding build
steps, try to select tasks from different domains and scenarios, and use
different types of tools to make the new sample different from the reference
sample in terms of task content and context. We start generating new single
samples corresponding to the dimensions. Special Environment Constraints:
Operating System (OS) generation sample (one): ==== Sample Start ==== Figure
12: Generation prompt for potential tools scenario, OS subtask. Potential
Tools: Web - Generation Prompt Task Description: You need to go ahead and
generate samples of tool usage with as much diversity as possible, based on
the current dimension, the requirements requirements of the task, and the
sample references provided. Current dimension: Application Environment
Dimension - Special Environment Constraints: Web Task Requirement: \- Generate
new samples taking into account as much as possible the diversity of user
query and available tools. \- Use "// Raw Input //" and "// Raw output //" to
label the inputs and outputs of the samples. \- The samples need to consider
as much as possible a scenario that could happen in reality, and multiple
tools need to be available in the raw input. In the current dimension, the
task description is in a web-related environment, including but not limited
to: shopping price comparison, web content retrieval, web data collection,
website usability evaluation, website security evaluation, website
optimization, etc. At build time, you need to: Give an overall task first,
then express the specific task requirements separately, and make sure that
they are as relevant to the web environment as possible. In the list of
available tools, make sure that you provide tools that can accomplish these
requirements, while including a small number of tools that do not directly
help the task. Next, repeat certain requirements once with different
expressions to ensure diversity in task complexity and tools. Finally,
complete the corresponding task planning process in the output based on these
requirements. Sample Reference: ==== Reference Sample Start ==== // Raw Input
// [sample_input] // Raw output // [sample_outputl] ==== Reference Sample End
==== Now, please combine the reference sample and the corresponding build
steps, try to select tasks from different domains and scenarios, and use
different types of tools to make the new sample different from the reference
sample in terms of task content and context. We start generating new single
samples corresponding to the dimensions. Special Environment Constraints: Web
generation sample (one): ==== Sample Start ==== Figure 13: Generation prompt
for potential tools scenario, Web subtask, Limited Functionality Tools:
Iterative - Generation Prompt Task Description: You need to go ahead and
generate samples of tool usage with as much diversity as possible, based on
the current dimension, the requirements requirements of the task, and the
sample references provided. Current dimension: Iterative Dimension - The task
requires repeated invocations of the same tool, with multiple iterations to
get the final result Task Requirement: \- Generate new samples taking into
account as much as possible the diversity of user query and available tools.
\- First ensure that the task description is a goal that requires the use of
multiple tools to accomplish, and provide the corresponding tools in the list
of available tools using numerical numbers. Then make sure that one tool is
called more than twice in a row during the planning phase, and that this row
of calls is reflected in the task planning. \- Use "// Raw Input //" and "//
Raw output //" to label the inputs and outputs of the samples. \- The samples
need to consider a realistic possible scenario as much as possible. Multiple
tools need to be available in the list of available tools in the raw input.
Note that in the current dimension, the raw output has multiple iterative
calls to the same tool in the planning phase, with at least two or more steps
in between, corresponding to the iterative call dimension. At build time, you
need to: First give a list of overall tasks and then express specific task
requirements separately. In the list of available tools, make sure to provide
tools that can fulfill these requirements, while including a small number of
tools that do not directly help the task to ensure task complexity and tool
diversity. Second, by placing certain limitations on the tool capabilities
that result in multiple invocations to accomplish a given requirement, and
making these limitations and requirements explicit in the task description and
the list of provided tools. Finally, the corresponding task planning process
is completed in the output based on these requirements. Sample Reference: ====
Reference Sample Start ==== // Raw Input // [sample_input] // Raw output //
[sample_outputl] ==== Reference Sample End ==== Now, please combine the
reference sample and the corresponding build steps, try to select tasks from
different domains and scenarios, and use different types of tools to make the
new sample different from the reference sample in terms of task content and
context. We start generating new single samples corresponding to the
dimensions. Iterative dimension generation sample (one): ==== Sample Start
==== Figure 14: Generation prompt for limited functionality tools scenario,
iterative subtask. Limited Functionality Tools: Optimal Tool Selection -
Generation Prompt Task Description: You need to go ahead and generate samples
of tool usage with as much diversity as possible, based on the current
dimension, the requirements requirements of the task, and the sample
references provided. Current dimension: Multiple Appropriate Tools Dimension -
Optimal Tool Selection Task Requirement: \- Generate new samples taking into
account as much as possible the diversity of user query and available tools.
\- First, make sure that the task description is a goal that requires the use
of multiple tools to accomplish, and provide the corresponding tools in the
list of available tools using numerical numbers. \- Use "// Raw Input //" and
"// Raw output //" to label the inputs and outputs of the samples. \- The
samples need to consider a realistic scenario as much as possible. Multiple
tools need to be available in the list of available tools in the raw input.
Note that there will be multiple suitable tools that all take on the same or
very similar functions in the current dimension, e.g., for a language
detection task, tool A may have a higher recognition rate for some minor
languages, while tool B is more accurate on common languages. One needs to be
weighed against the specific needs of the task (whether comprehensiveness is
more important or accuracy for a particular language is more important); for a
text sentiment analysis task, tool C can provide simple keyword-based
sentiment analysis, while tool D is capable of deeper sentiment understanding
and contextual analysis. In this case, if the user needs a quick and shallow
analysis, tool C may be preferred; if deeper sentiment understanding is
needed, tool D may be preferred. One needs to choose the right tool based on
the specific situation, e.g., the complexity of the text and the detailed
needs of the user; and so on. At build time, you need to: First give a list of
overall tasks and then express specific task requirements separately. In the
list of available tools, make sure to provide tools that can fulfill these
requirements, while adding a small number of tools that do not directly help
the task to ensure the complexity of the task and the diversity of tools. At
the same time, in the current dimension, you need to build multiple tools with
the same functionality but different focuses, and emphasize a particular focus
in the task description to achieve the effect of selecting the most effective
tool. Finally, according to these requirements, complete the corresponding
task planning process in the output, keeping in mind that each step
corresponds to a different tool. Sample Reference: ==== Reference Sample Start
==== // Raw Input // [sample_input] // Raw output // [sample_outputl] ====
Reference Sample End ==== Now, please combine the reference sample and the
corresponding build steps, try to select tasks from different domains and
scenarios, and use different types of tools to make the new sample different
from the reference sample in terms of task content and context. We start
generating new single samples corresponding to the dimensions. Multiple
Appropriate Tools Dimension - Optimal Tool Selection generation sample (one):
==== Sample Start ==== Figure 15: Generation prompt for limited functionality
tools scenario, optimal tool selection subtask. Evaluation Prompt for Level-1
Solvability Detection Determines whether the current task can be solved with
the tools provided in <provided_tools>, based on the task description in
<task>. Note that you need to carefully review the requirements, limitations
of the <task>, and capability descriptions of the tools in <provided_tools>,
and that you can’t use tools that aren’t provided in <provided_tools>. You
need to wrap your answer in <answer> and </answer>. If the task can be solved,
output ’<answer>solvable</answer>’. If the task is not solvable, output
’<answer>unsolvable</answer>’. Other than that, do not output anything extra.
<task> [task_description] </task> <provided_tools> [provided_tool_list]
</provided_tools> Figure 16: Evaluation Prompt for Level-1 Solvability
Detection Evaluation Prompt for Level-2 Solution Planning Plan your tool usage
based on the task description in <task> and the available tools provided in
<provided_tools>. List the names of the tools you need to use to complete the
task in order, wrapping them in <answer> and </answer> and separating each
tool with a line break “$\backslash$n”, e.g.:
<answer>Tool1$\backslash$nTool2$\backslash$n… $\backslash$nTooln</answer> If a
step of the task does not have a tool that can be solved in the list of tools
provided by <provided_tools>, use the UnsolvableQuery tool at the
corresponding step, and then continue the planning for the remaining steps,
e.g.:: <answer>Tool1$\backslash$n… $\backslash$nUnsolvableQuery$\backslash$n…
$\backslash$nTooln</answer> If there is a limit to the number of tools that
can be used in <task>, call UnsolvableQuery to terminate the task on the next
step after the limit is reached, e.g., to limit the number of tools to t or
less: $\backslash$n<answer>Tool1$\backslash$n…
$\backslash$nToolt$\backslash$nUnsolvableQuery</answer> In a wrapped answer,
do not output any redundancy other than the name of the tool, and do not
interpret the output in any way. You cannot use tools that are not provided in
<provided_tools>. Ensure that the tool names are clear and separated for each
step. Output only the one answer you think is most correct. <task>
[task_description] </task> <provided_tools> [provided_tool_list]
</provided_tools> Figure 17: Evaluation Prompt for Level-2 Solution Planning
Evaluation Prompt for Level-3 Missing-Tool Analysis Divide the task and
develop a task plan for tool usage based on the task description in <task> and
the tools in <provided_tools>. Requirements: 1\. Each step of the task
planning based on the tools provided in the list of available tools may
consist of multiple steps (t>=1), with each step corresponding to a sub-
objective and a tool usage. 2\. Sub-objectives need to be logically related to
each other to ensure that the completion of sub-objectives advances the
overall task progress. 3\. Tools that are not in <provided_tools> cannot be
used 4\. Use <answer> and </answer> to wrap the entire answer The format for
task planning can be seen in the following example: <answer>Subgoal 1:
[description] Planned tool: [tool name] Subgoal 2: [description ] Planned
tool: [tool name] … Subgoal t: [description] Planned tool: [tool
name]</answer> nState the relationship between the subgoal and the requirement
in the [description] section, provide the tool name in the [tool name]
section, and don’t provide any description or explanation in the [tool name]
section. If there is a limit to the number of tools that can be used in a
<task>, the next step after the limit is reached calls UnsolvableQuery to
terminate the task. For example, to limit the number of tools to t or less:
<answer>Subgoal 1: [description] Planned tool: [tool name] … Subgoal t:
[description] Planned tool: [tool name] Subgoal t+1: [Task requires number of
tools within t] Planned tool: UnsovlableQuery</answer> Now, let’s start
scheduling the current task, using <answer> to mark the start of the task and
</answer> mark the end of the task. <task> [task_description] </task>
<provided_tools> [provided_tool_list] </provided_tools> Figure 18: Evaluation
Prompt for Level-3 Missing-Tool Analysis