# Interpretable Cross-Examination Technique (ICE-T): Using highly informative
features to boost LLM performance

Goran Muric
InferLink Corporation
Los Angeles, California
<EMAIL_ADDRESS>
Ben Delay
InferLink Corporation
Los Angeles, California
<EMAIL_ADDRESS>
Steven Minton
InferLink Corporation
Los Angeles, California
<EMAIL_ADDRESS>

###### Abstract

In this paper, we introduce the Interpretable Cross-Examination Technique
(ICE-T), a novel approach that leverages structured multi-prompt techniques
with Large Language Models (LLMs) to improve classification performance over
zero-shot and few-shot methods. In domains where interpretability is crucial,
such as medicine and law, standard models often fall short due to their
“black-box” nature. ICE-T addresses these limitations by using a series of
generated prompts that allow an LLM to approach the problem from multiple
directions. The responses from the LLM are then converted into numerical
feature vectors and processed by a traditional classifier. This method not
only maintains high interpretability but also allows for smaller, less capable
models to achieve or exceed the performance of larger, more advanced models
under zero-shot conditions. We demonstrate the effectiveness of ICE-T across a
diverse set of data sources, including medical records and legal documents,
consistently surpassing the zero-shot baseline in terms of classification
metrics such as F1 scores. Our results indicate that ICE-T can be used for
improving both the performance and transparency of AI applications in complex
decision-making environments.

Interpretable Cross-Examination Technique (ICE-T): Using highly informative
features to boost LLM performance

Goran Muric InferLink Corporation Los Angeles, California<EMAIL_ADDRESS>Ben Delay InferLink Corporation Los Angeles, California<EMAIL_ADDRESS>Steven Minton InferLink Corporation Los Angeles, California
<EMAIL_ADDRESS>

##  1 Introduction

There are numerous prompting strategies to achieve good performance using
generative Large Language Models (LLMs). Take, for instance, a binary
classification problem, where a system should classify the given text into one
of two classes. A typical zero-shot approach is to prompt the model with a
given text and carefully designed question, that will yield an appropriate
answer. There are also multiple variations on that approach that include
“chain-of-thought” prompting Wei et al. (2022c); Wang et al. (2022a); Kojima
et al. (2022), “few-shot learning” Schick and Schütze (2022); Gu et al.
(2021), “self-instruct” Wang et al. (2022b); Yang et al. (2024) prompting and
“iterative refinement” Wu et al. (2022a); Trautmann (2023). These tactics are
used to get a better sense of the model’s underlying reasoning or to surpass
the performance achieved by the standard zero-shot method.

These options are usually used in cases where using highly specialized fine-
tuned LLMs is not a viable option because it is often of utmost importance to
understand how decisions are made. This is especially true in fields like
medicine, where decisions based on opaque, “black-box” models are usually not
acceptable. Although zero-shot or few-shot prompting methods can potentially
offer explanations for their reasoning, these explanations are often
unstructured and lack quantifiability. On the other hand, while finely tuned
models may achieve superior performance, they frequently struggle to
articulate the rationale behind their outputs unless explicitly trained for
this purpose, a process that is labor-intensive. Additionally, outputs from
such models may also suffer from the lack of structured reasoning
representation.

In cases where using “black-box” models is not practical, and where
interpretability is important, users have the option to develop a structured
reasoning process by asking several questions to achieve a desired output.
There are three main problems that arise with this approach: 1) Non-experts
have little chance to develop a good set of questions and rules that ensure
optimal model performance; 2) Designing an accurate rule set becomes
challenging since individual instances may not perfectly align with all
desired criteria, resulting in a mix of positive and negative responses to
different rules; and 3) The potential combinations of these rules can become
overwhelmingly numerous, making it impractical to hard-code every possible
scenario.

In the paper, we propose a method that attempts to overcome the three issues
outlined above. We refer to the method as the Interpretable Cross-Examination
Technique, or ICE-T for brevity. Our approach exhibits strong performance,
consistently surpasses the benchmark set by a zero-shot baseline, and also
offers a high level of interpretability. The core concept here is that rather
than using a single prompt to get a response from an LLM and making a decision
based on that single output, we engage the LLM with multiple prompts, covering
various questions. We then combine the responses from all these prompts and
use the outputs to make a decision. Compared to other methods that are based
on multi-prompting, our approach is fundamentally different in the way the
decisions are made. Specifically, we take the responses from the LLM, convert
them into numerical values to create a feature vector, and then input this
vector into a traditional classifier to determine the final outcome. Since, in
this process we create a low-dimensional feature vector with highly
informative features, we can then use relatively small classifiers to make a
decision.

We established an experimental setup where we tested our Interpretable Cross-
Examination Technique on a simple binary classification task. We tested our
approach on a set of multiple datasets split on 17 different tasks and we show
that:

  1. 1.

ICE-T consistently outperforms the zero-shot baseline model in most
classification metrics

  2. 2.

Using a smaller model with ICE-T we can achieve comparable or better results
than using larger and essentially more capable model with zero-shot approach

Furthermore, this approach can be highly interpretable, allowing experts to
clearly understand the rationale behind the decision-making process111Degree
of interpretability may vary depending on the machine learning method selected
for the final classification task. The decision on which method to employ
should be guided by a consideration of the trade-offs between interpretability
and performance tailored to the unique demands of each task. Additionally,
tools commonly used for tabular machine learning can be employed to enhance
the understanding of the data. While this technique is specifically evaluated
for binary classification within this paper, its applicability potentially
extends across a broad spectrum of scenarios.

###  1.1 Motivation

The ICE-T method was initially conceived at InferLink in a commercial
consulting project, where we needed to address a complex challenge in
biomedical text classification. The project’s goals were to develop a model
that could perform at a level comparable to human experts, provide
interpretable results, and allow for detection of potentially mislabelled
data. Initially, conventional “black-box” models such like fine-tuned BERT-
based ones underperformed, as well as zero-shot or few-shot learning methods
using LLMs. This led to the creation of the ICE-T, which improved the
performance of classification, while gaining interpretability and allowing for
the correction of labeling errors. ICE-T was used initially for the purpose of
classifying biomedical data for a specific commercial purpose. While the
specifics of this initial task and data remain confidential, we have conducted
further testing on additional publicly available datasets and decided to make
the method publicly accessible.

##  2 Related Work

Our proposed solution addresses three core aspects of using large language
models for inference: prompting, in-context learning, and interpretability. It
is built on top of the ever-growing body of knowledge that comes from those
domains.

###  2.1 Prompting techniques

Numerous techniques have been developed to improve the fundamental zero-shot
approach. Among these, the “chain-of-thought” (CoT) prompting is particularly
notable. This method is used to prompt the model to systematically articulate
its reasoning process in a step-by-step manner before reaching a conclusion.
Research has shown that chain-of-thought prompting improves performance on a
range of arithmetic, commonsense, and symbolic reasoning tasks Wei et al.
(2022b, c); Wang et al. (2022a). Even simple tweaks such as adding “Let’s
think step by step” before each answer can significantly outperform zero-shot
LLM performances on diverse benchmark reasoning tasks Kojima et al. (2022);
Nye et al. (2021). Such generated chains that prompt language models to break
down their reasoning into steps often cause errors in inference time. To
reduce these errors, some researchers employ a method known as automatic Chain
of Thought prompting. This technique, which generates demonstrable examples,
has proven to be more effective than earlier, simpler CoT approaches Zhang et
al. (2022b). Lastly, “iterative refinement” involves repeatedly prompting the
model with slightly altered versions of the original text or question, honing
in on a more accurate or nuanced answer through successive iterations. Each of
these strategies can be tailored to the specific needs of a task, leveraging
the model’s capabilities in different ways to achieve optimal performance.

Several approaches involve using multiple prompts in a chain, where the output
of one step becomes the input for the next, thus aggregating the gains per
step Wu et al. (2022a), or decomposing complex tasks into smaller, manageable
components Trautmann (2023). Additionally, “self-instruct” Wang et al.
(2022b); Yang et al. (2024) prompting can be used, where the model generates
its own instructions or clarifications based on the initial prompt, attempting
to refine or better understand the task before generating a response. Another
set of approaches uses multiple models or multiple instances of the same model
to improve the performance. The additionally trained models, called
“verifiers” are used to judge the correctness of model completions. At the
inference time, the verifiers would select the most likely answer Cobbe et al.
(2021).

###  2.2 In-context learning

Large Language Models possess the remarkable ability for in-context learning
(ICL), in which they acquire knowledge from a few contextual examples either
during inference or during training. Numerous studies have shown that through
ICL, LLMs can effectively handle a diverse set of complex tasks Wei et al.
(2022a). ICL offers several advantages, notably its ease in including human
knowledge into LLMs by using various demonstrations and templates Liu et al.
(2021); Wu et al. (2022b). Furthermore, unlike traditional supervised training
methods, ICL operates without the need for additional training, significantly
lowering the computational costs when using models to solve new tasks Dong et
al. (2022).

One of the most recognizable techniques for in-context learning is “few-shot
learning” Schick and Schütze (2022, 2020); Gu et al. (2021); Perez et al.
(2021) during inference222A different approach to achieving few-shot learning
can occur also during the training phase or during fine-tuning.. Using this
approach, the model is provided with a few examples of text and their
corresponding labels or desired outputs within the prompt itself. This method
teaches the model the context of the decision-making process, improving its
accuracy on similar tasks.

Multiple other studies contributed to refining the ICL methods, focusing on
automation, ordering, and selection of prompts. Zhou et al. (2022) introduced
the Automatic Prompt Engineer (APE), which automates the generation of
instructional prompts, significantly reducing manual effort and improving
scalability Zhou et al. (2022). Simultaneously, Lu et al. (2021) came up with
the method to optimize the ordering of prompts. They employed entropy
statistics to evaluate and identify the most effective prompt sequences Lu et
al. (2021). Rubin et al. (2021) and Liu et al. (2021) both contribute to this
area but from different perspectives. Rubin et al. (2021) developed a method
for efficiently retrieving prompts using annotated data, streamlining the
selection process Rubin et al. (2021). On the other hand, Liu et al. (2021)
explored strategic selection methods that go beyond random sampling to
leverage the few-shot capabilities of LLMs, aiming to enhance the model’s
performance through example selection Liu et al. (2021). Adding to the
discussion on selection strategies, Zhang et al. (2022) approached example
selection as a sequential decision problem. They proposed using a
reinforcement learning algorithm to discover policies that improve the
generalizability of language models Zhang et al. (2022a). This perspective
introduces a dynamic element to the selection process, aligning with the
strategies discussed by Rubin and Liu but through an adaptive, policy-driven
approach.

###  2.3 Model interpretability

The challenge of interpreting complex decision processes made by LLMs has
hindered their application in critical areas like medicine, where there are
significant concerns about regulation Goodman and Flaxman (2017) and safety
Amodei et al. (2016). Furthermore, this difficulty in understanding the
workings of large language models (LLMs) and similar neural network models has
restricted their use in domains like science and data analysis Kasneci et al.
(2023). In such fields, the primary objective is often to derive a reliable
interpretation rather than merely to implement an LLM Singh et al. (2024).

The expression of uncertainty in language models is crucial for reliable LLM
utilization, yet it remains a challenging area due to inherent overconfidence
in model responses. Xiong et al. (2023) and Zhou et al. (2024) both highlight
the overconfidence issue in LLMs. Xiong et al. question whether LLMs can
express their uncertainty, observing a tendency in LLMs to mimic human
patterns of expressing confidence Xiong et al. (2023). Simlarly, Zhou et al.
note that while LLMs can be prompted to express confidence levels, they remain
generally overconfident and unable to convey uncertainties effectively, also
when providing incorrect responses Zhou et al. (2024). Ye et al. (2022) add
that even when LLMs generate explanations, these may not accurately reflect
the model’s predictions nor be factually grounded in the input, particularly
in tasks requiring extractive explanations Ye and Durrett (2022). However, all
the research mentioned above note that these flawed explanations can still
serve a purpose, offering a means to verify LLM predictions post-hoc.

It is worth mentioning feature attribution methods, used beyond the LLM realm
in multiple deep-learning applications. Feature attributions in machine
learning provide a relevance score to each input feature, reflecting its
impact on the model’s output. This methodology helps in understanding how and
why certain decisions or predictions are made by a model.

The approaches developed by Lundberg et al. (2017) and Sundararajan et al.
(2017) both delve into this topic but offer distinct methodologies and
theoretical foundations. Lundberg et al. Lundberg and Lee (2017) introduced
SHAP (SHapley Additive exPlanations), which provides a unified framework for
interpreting predictions. SHAP assigns an importance value to each feature for
a specific prediction, leveraging the concept of Shapley values from
cooperative game theory. In contrast, Sundararajan et al. Sundararajan et al.
(2017) developed Integrated Gradients, another method focusing on the
attribution of predictions to input features of deep networks. Unlike SHAP,
which uses Shapley values, Integrated Gradients relies on the integration of
gradients along the path from a chosen baseline to the actual input.
Complementing these approaches, Ribeiro et al. (2016) proposed LIME (Local
Interpretable Model-agnostic Explanations), which aims to make the predictions
of any classifier understandable and reliable by learning an interpretable
model localized around the prediction Ribeiro et al. (2016).

Another popular method for understanding neural-network representations is
probing. Conneau et al. (2018) initially introduced multiple probing tasks
designed to capture simple linguistic features of sentences, setting a
foundation for understanding how neural networks encode linguistic properties
Conneau et al. (2018).

Clark et al. (2019) focused primarily on the behavior of attention heads
within transformers. They observed that these heads often broadly attend
across entire sentences, and that attention patterns in the same layer tend to
exhibit similar behaviors. Crucially, their research links specific attention
heads to traditional linguistic concepts like syntax and coreference,
suggesting a direct relationship between the model’s attention mechanisms and
linguistic structures Clark et al. (2019), although there is an ongoing debate
on the explanatory power of attention in neural network Bibal et al. (2022).
Unlike Clark et al., who examine what the model attends to, Morris et al.
Morris et al. (2023) explore how information is preserved and can be retrieved
from embeddings, offering insights into the reversibility and fidelity of the
encoding process. Their method involves a multi-step process that iteratively
corrects and re-embeds text, demonstrating the ability to recover most of the
original text inputs exactly. Belrose et al. (2023) introduced a technique
called causal basis extraction, which aims to identify influential features
within neural networks Belrose et al. (2023). This method stands out by
focusing on the causality within network decisions.

In summary, while chain-of-thought prompting can generate errors during
inference, requiring complex corrective approaches, in-context learning
techniques also face challenges in prompt optimization and efficient
retrieval. Furthermore, interpreting large language models remains
problematic, exacerbated by models’ tendency to exhibit overconfidence and
provide unreliable or unverifiable explanations.

##  3 Method

Training the ICE-T system consists of the following steps:

  1. 1.

Generating questions: the process begins by generating a series of questions
designed to prompt the Large Language Model (LLM);

  2. 2.

Prompting the LLM: Previously generated questions are used to prompt the LLM
and collect the yes/no answers;

  3. 3.

Verbalizing the answers: for each instance within the training dataset,
responses to prompts are collected and converted into numerical form, thus
creating a low-dimensional feature vector for each instance;

  4. 4.

Training a classifier: Previously obtained vectors, together with their
respective labels, are then used to train a classifier

The Inference stage mirrors the training process: the LLM is presented with
the same collection of questions. The responses obtained are numerically
encoded in the same manner before being processed by the classifier that was
trained during the Training stage. Training and inference process is
illustrated in Figure 1. Each step is explained below.

###  3.1 Generating questions

To train and use the system, we need to create multiple questions that more
closely reflect the core principles behind the initial yes/no question. Those
questions should be crafted in a way to uncover some additional details about
the problem.

Consider a use case where an expert is building a classifier to determine
eligibility for medical trials based on patient data. In such a scenario, the
classifier needs to assess various clinical inclusion criteria, which are
typically derived from patient medical records. One of these criteria could be
the patient’s language proficiency, for instance, whether they speak English.
A naive formulation of this question may be to present the question to the LLM
in a prompt like the following:

      Does this patient speak English according to
      their medical records.
      MEDICAL RECORDS: __RECORDS__

where the `__RECORDS__` represents the appended textual medical records.
Determining the answer to that question, which we call the “primary” question,
may not be easy given the medical records under consideration, requiring an
understanding of somewhat subtle indicators that show if a patient actually
speaks English. It is highly unlikely that the medical records will directly
state the answer to that question.

However, a series of “secondary” questions such as:

      Is there any documentation of the patient
        requiring an interpreter for English
        during medical visits?
      Do the medical records contain notes written
        in English that indicate communication
        with the patient?
      Are there any written consents or forms
        completed in English by the patient?
      Are there any notations from providers about
        the patient’s ability to understand and
        speak English?

may allow the model to answer directly based on the information already
contained in the documents presented to it, while also serving as strong
indicators for the primary question. Secondary questions are also yes/no
questions.

Creating the secondary questions can be done in multiple ways, such as writing
the questions manually using the expert knowledge or using the LLM to
automatically generate a fixed size set of questions that might be useful in
answering the original question. Starting from the primary question $q_{0}$ we
generate $n$ additional questions, creating a set of all questions
$Q=\\{q_{0},q_{1}\ldots q_{n}\\}$, where $|Q|=n+1$. This process is shown in
Figure 1 with a red box, illustrating the creation of the questions and using
them during the training and inference process. The same set $Q$ of questions
is used for both training and inference.

The number $n$ of secondary questions is decided based on factors such as:
number of training samples, availability of the expert knowledge and the level
of interpretability needed for a specific task. Our prior small-scale
experiments have shown that secondary questions crafted by experts generally
lead to improved performance compared to those generated by LLMs. However, in
the experiments reported here, we chose a straightforward and reproducible
approach where we exclusively use secondary questions created by an LLM. This
choice was made to minimize human bias and showcase the method’s effectiveness
in scenarios where expert input is unavailable. The exact prompts used for
creating secondary questions in our experiments are described in Section 5.

Figure 1: Illustration of training and inference process in ICE-T. In the
training phase, the process begins by generating questions to prompt an LLM,
which then provides yes/no answers. These answers are verbalized and converted
into numerical feature vectors. A classifier is trained using these vectors
along with their respective labels. During inference, the LLM is prompted with
the same questions, and the answers are similarly processed to predict
outcomes using the trained classifier.

###  3.2 Prompting LLM

The LLMs are prompted in two occasions. First, they are prompted to obtain the
set of secondary questions $Q$, as described in Section 3.1. Second, for each
document, we prompt the LLM with the document and corresponding secondary
questions. Then, for each question $q_{i}$ the output $a_{i}$ of the LLM is
collected, creating a set of outputs for each document. The textual outputs
are then assigned a numerical value and transformed into a feature vector
$v_{i}$, through the verbalization process explained in Section 3.3.

###  3.3 Verbalizing the answers

The output of the LLM in response to each prompt is limited to one of three
possible values: Yes, No, or Unknown, depending on the answer to the question
posed in the prompt. These responses are subsequently assigned numerical
values for analysis, with “Yes” translating to 1, “No” to 0, and “Unknown” to
0.5.

###  3.4 Training a classifier

To train a classifier, we use a set $V$ of low-dimensional numerical vectors,
where $|V|=n+1$ and corresponding labels $X$, where each vector $v_{i}$ has a
corresponding binary label $x_{i}$. Vectors $V$ are obtained from the training
textual data after prompting LLM to generate $n+1$ outputs that are then
assigned a numerical value. A classifier is then trained using a 5-fold cross-
validation process and grid search for the best parameters. A choice of a
specific classification algorithm will depend on the size of training data,
values distribution and desired performance on a specific classification
metric.

##  4 Data

This work utilizes data compiled from a range of sources, attempting to
include a variety of domains and document lengths. The data used in the
experiments described here spans the fields of medicine, law, climate science,
and politics. It also includes documents of varying sizes, from brief tweets
to extensive legal documents and detailed medical records.

###  4.1 Clinical trials

This dataset comes from Track 1 of the 2018 National NLP Clinical Challenges
(n2c2) shared tasks333https://n2c2.dbmi.hms.harvard.edu/. It is designed to
help in identifying patients within a corpus of longitudinal medical records
who either meet or do not meet predefined selection criteria. These criteria
are used for determining a patient’s eligibility for inclusion in clinical
trials. Stubbs et al. (2019). The data consists of annotated American English
clinical narratives for 288 patients according to whether they met a set of
specific criteria. There are 13 criteria in total, and they include: DRUG-
ABUSE: Drug abuse, current or past; ALCOHOL-ABUSE: Current alcohol use over
weekly recommended limits; ENGLISH: Patient must speak English; MAKES-
DECISIONS: Patient must make their own medical decisions; ABDOMINAL: History
of intra-abdominal surgery, small or large intestine resection, or small bowel
obstruction; MAJOR-DIABETES: Major diabetes-related complication; ADVANCED-
CAD: Advanced cardiovascular disease (CAD); MI-6MOS: MI in the past 6 months;
KETO-1YR: Diagnosis of ketoacidosis in the past year; DIETSUPP-2MOS: Taken a
dietary supplement (excluding vitamin D) in the past 2 months; ASP-FOR-MI: Use
of aspirin to prevent MI; HBA1C: Any hemoglobin A1c (HbA1c) value between 6.5%
and 9.5%; and CREATININE: Serum creatinine $>$ upper limit of normal. For
every medical record, each criterion can have one of two potential values:
“met” or “not met.” The value based on whether an individual has fulfilled a
particular criterion. Data is split 70/30 on training and test sets
respectively. Training test contains 202 medical record while the test set
contains 86 records. Note that for some criteria, the ratio between positive
and negative class is highly imbalanced. In our analysis we excluded KETO-1YR
criterion as it contains no positive samples in the test set and only one
positive sample in the training set.444Our methodology employs classifiers
that are trained based on data distributions. As a result, we consistently
achieve peak classification metrics, which is not a realistic performance, as
the minority class is absent from the test dataset.

###  4.2 Catalonia Independence Corpus

This dataset contains a corpus in Spanish that consist of annotated Twitter
messages for automatic stance detection Zotova et al. (2020). It encompasses
data collected over a 12-day span in February and March 2019, from tweets
originating in Barcelona. Originally, each tweet is categorized into one of
three classes: AGAINST, FAVOR, and NEUTRAL. These classes represent the user’s
stance towards the topic of Catalonia’s independence. For the purpose of
binary classification and to facilitate more effective comparisons with other
datasets, we have omitted the NEUTRAL class, focusing exclusively on the
AGAINST and FAVOR categories.

###  4.3 Climate Detection Corpus

This dataset contains climate-related paragraphs extracted from financial
disclosures by companies. The text has been collected from corporate annual
reports and sustainability reports. The paragraphs from those reports are
hand-selected and then annotated as yes (climate-related) or no (not climate-
related) Webersinke et al. (2021).

###  4.4 Medical health advice data

This dataset comprises a collection of sentences related to the medical
domain, each accompanied by a label indicating whether the sentence offers
medical advice. The labels can be one of three values: “strong advice”, “weak
advice”, or “no advice”. Yu et al. (2019) For the purpose of binary
classification task we combined “strong advice” and “weak advice” into a
single class: “advice”. The dataset includes approximately 8,000 samples,
which have been divided into training and test datasets following the 80/20
rule.

###  4.5 The European Court of Human Rights (ECtHR) Data

The European Court of Human Rights (ECtHR) hears allegations that a state has
breached human rights provisions of the European Convention of Human Rights
(ECHR) Chalkidis et al. (2019). The dataset for each case includes a series of
facts in form of paragraphs extracted from the case description. Additionally,
each case is associated with specific articles of the European Convention on
Human Rights (ECHR) that may have been violated. In many cases, multiple
articles are violated at the same time. To make this a binary categorization
problem, we adopted a binary labeling system. Cases are marked with a “1” if
any ECHR articles are violated, and a “0” if no violations are detected.

###  4.6 UNFAIR-ToS Dataset

The UNFAIR-ToS dataset contains 50 relevant on-line consumer contracts, i.e.
Terms of Service (ToS) from on-line platforms (e.g., YouTube, Ebay, Facebook,
etc.). Each agreement has been annotated at the sentence level to identify
various types of potentially unfair clauses, which could infringe upon user
rights under European consumer law. This dataset categorizes unfair terms into
eight distinct groups: Arbitration, Unilateral Change, Content Removal,
Jurisdiction, Choice of Law, Limitation of Liability, Unilateral Termination,
and Contract by Using Lippi et al. (2019). To transform the analysis into a
binary classification problem, we re-labelled each sentence as either “unfair”
if it contains any type of the identified unfair terms, or “not unfair’ if it
does not fall into these categories.

##  5 Experiments

We performed the experiments on a set of binary classification tasks on
datasets from various domains, as described in the previous section.

To generate the secondary questions, we employed a large language model.
Prompting it only once, we obtained a set of $n$ secondary questions, which we
accepted as provided, without any selection or modification. More
specifically, we used the following prompt for creating all secondary
questions:

        Return {n} yes/no questions that would be
        useful to ask if you were trying to
        determine the answer to the following
        question: "{primary_question}"

where $n$ is the number of additional questions we want to generate and
`primary_question` is the primary question used to obtain the main information
from the document. Note that in all our experiments $n=4$. That means that for
each document we use one primary and four secondary questions that are treated
equally when prompting the LLM. Thus, for each document we collect five
answers from the LLM that are then verbalized (assigned a numerical value) in
the next step. To generate the secondary questions for all our experiments we
use OpenAI’s `gpt-4-0125-preview` model. To collect the answers in our
experiments we use two generations of OpenAI’s models: `gpt-4-0125-preview`
Achiam et al. (2023) and `gpt-3.5-turbo-0125` Brown et al. (2020).

To choose the best classifier, we train several different classification
algorithms. These include K-Nearest Neighbors, Decision Trees, Random Forest,
Gaussian Naive Bayes, Multinomial Naive Bayes, AdaBoost, and XGBoost. We use a
5-fold cross-validation on our training data and also perform a grid search to
fine-tune the parameters for each classifier. After training, we test them on
a hold-out test set and choose the classifier that gives us the highest Micro
F1 score ($\mu F1$). Note that one can also adjust the training process to
optimize for a specific performance metric if needed for a particular
application. To perform these experiments, we used the `scikit-learn` library
in Python.

Micro F1 score is particularly useful in datasets where some classes are
significantly underrepresented, and where traditional metrics might give a
misleading picture of model performance. It treats every instance as equally
important, thereby giving a more accurate measure of the model’s performance
across the board. To calculate the $\mu F1$, we use the following formula:

$\mu F1=\frac{2\mu Precision\times\mu Recall}{\mu Precision+\mu Recall}$ (1)

where

$\mu Precision=\frac{\sum{TP_{i}}}{\sum{TP_{i}+\sum{FP_{i}}}}$ (2)

$\mu Recall=\frac{\sum{TP_{i}}}{\sum{TP_{i}+\sum{FN_{i}}}}$ (3)

and $TP$, $FP$ and $FP$ represent number of true positives, false positives
and false negatives respectively.

Additionally, we conducted a sensitivity analysis to enhance our understanding
of the relationship between the number of features and the improvement of the
$\mu F1$. This analysis helps determine the requisite number of secondary
questions to attain a desired $\mu F1$. For each dataset, we started by
creating $n=9$ secondary questions and using the `gpt-3.5-turbo-0125` model to
generate responses for each sample. The outputs from the large language model
were then transformed into 10-dimensional feature vectors. Subsequently, we
constructed a series of simple Random Forest classifiers, starting with a
single feature and incrementally adding more features up to ten. Given the
random selection of features for classification, we repeated the experiment
100 times. We computed the $\mu F1$ for each iteration and dataset. The
findings are detailed in Section 6 and illustrated in Figure 3.

##  6 Results

The results of the classification experiments are summarized in Table 1. We
can see that across all datasets, the ICE-T method consistently surpasses the
zero-shot approach in performance for a given language models. Specifically,
using the GPT-3.5 model, the average $\mu F1$ for the zero-shot approach is
0.683, but it increases to 0.845 with the ICE-T method. A similar trend is
observed with the larger GPT-4 model, where the average F1 score improves from
0.7 using the zero-shot approach to 0.892 with the ICE-T technique. This
improvement is not constant across the datasets as we can see a significant
variations in performance and in improvements across different tasks.

| GPT-3.5 | GPT-4
---|---|---
| 0-shot | ICE-T | 0-shot | ICE-T
ABDOMINAL | 0.791 | 0.814 | 0.802 | 0.884
ADVANCED-CAD | 0.640 | 0.756 | 0.791 | 0.907
ALCOHOL-ABUSE | 0.814 | 0.965 | 0.791 | 0.965
ASP-FOR-MI | 0.849 | 0.86 | 0.860 | 0.895
CREATININE | 0.349 | 0.721 | 0.488 | 0.872
DIETSUPP-2MOS | 0.593 | 0.767 | 0.488 | 0.814
DRUG-ABUSE | 0.942 | 0.977 | 0.826 | 0.977
ENGLISH | 0.919 | 0.988 | 0.233 | 0.965
HBA1C | 0.477 | 0.837 | 0.523 | 0.942
MAJOR-DIABETES | 0.733 | 0.814 | 0.570 | 0.884
MAKES-DECISIONS | 0.605 | 0.965 | 0.663 | 0.965
MI-6MOS | 0.651 | 0.919 | 0.849 | 0.953
Catalonia indep. | 0.528 | 0.579 | 0.562 | 0.604
Climate detection | 0.702 | 0.8 | 0.912 | 0.925
ECtHR | 0.853 | 0.873 | 0.861 | 0.873
Health advice | 0.836 | 0.841 | 0.846 | 0.846
UNFAIR-ToS | 0.335 | 0.887 | 0.837 | 0.889
Average | 0.683 | 0.845 | 0.700 | 0.892
Table 1: Comparison of $\mu F1$ scores between zero-shot setting and ICE-T
method. The values in bold represent the $\mu F1$ score of the winning
approach for a specific task and a language model. Horizontal line in the
middle splits the clinical trial datasets and other datasets. All tasks solve
the binary classification problem.

The upper portion of Table 1 showcases the findings from the clinical trial
dataset, as detailed in Section 4. The dataset’s contents remain consistent
across all sub-tasks within this clinical trial dataset, though each sub-task
involves a distinct classification criterion based on 12 different criteria.
In some sub-tasks, substantial improvements were observed over the zero-shot
method. For instance, in the task CREATININE (involving serum creatinine
levels exceeding the upper normal limit), the zero-shot method achieved $\mu
F1$ of 0.349. In contrast, the ICE-T technique utilizing the same large
language model significantly improved this score to 0.721. Similarly, for the
task ENGLISH (determining if a patient speaks English) using the larger GPT4
model, the greatest increase noted exceeded 0.733 points, with the zero-shot
approach at a $\mu F1$ of 0.233 and the ICE-T technique improving it to 0.966.
Analysis of tasks outside the clinical trial dataset revealed varied results,
dependent on the specific domain. The task assessing “Catalonia independence”
presented a notable challenge in the zero-shot setup for both models, barely
achieving a $\mu F1$ above 0.5, with no significant improvements noted with
the ICE-T technique.

The task related to the European Court of Human Rights (ECtHR) already
exhibited high baseline scores in the zero-shot setting, achieving 0.853 with
GPT-3.5 and 0.861 with GPT-4. The application of the ICE-T technique yielded
minimal improvement, with both models achieving a $\mu F1$ of 0.873. A similar
scenario was observed with the Health advice dataset, where enhancements were
negligible.

However, the UNFAIR-ToS task demonstrated significant improvement using the
ICE-T approach, particularly with the GPT-3.5 model. Here, the $\mu F1$ score
saw a dramatic increase from 0.335 to 0.887.

Furthermore, our analysis reveals that the ICE-T technique, when applied to a
smaller model, can surpass or match the performance of a larger model that
uses the zero-shot approach. In our experiments, we assessed the $\mu F1$ of
classification tasks executed by GPT-4 in a zero-shot setting against those
performed by GPT-3.5 using the ICE-T technique across various datasets. In
nearly all cases, except for two, the ICE-T-enhanced GPT-3.5 either
outperformed or equaled the larger GPT-4 model on identical tasks. These
findings are depicted in Figure 2.

Figure 2: Comparative performance of ICE-T-enhanced GPT-3.5 versus zero-shot
GPT-4. The figure illustrates the $\mu F1$ achieved by GPT-3.5 utilizing the
ICE-T technique and GPT-4 in a zero-shot setting across multiple datasets.

We observed a minor variation in performance across different task groups. By
categorizing clinical trial tasks into one group and other tasks into another,
we observed a comparable average performance improvement when comparing the
zero-shot to the ICE-T approach, as detailed in Table 4 in Appendix B. This
consistency underscores the versatility of the ICE-T method across various
domains and tasks.

To explore how the number of features impacts the micro F1 score ($\mu F1$),
we conducted an additional sensitivity analysis. The outcomes of this analysis
are depicted in Figure 3. This figure illustrates the change of the $\mu F1$
as we incrementally introduce more features (obtained by secondary questions).
A solid orange line shows the average $\mu F1$ across all datasets, while the
surrounding shaded area indicates one standard deviation from the mean, based
on 100 iterations. As anticipated, there is a consistent increase in the micro
F1 score with the addition of more secondary questions. On average, adding
three secondary questions increases the $\mu F1$ score from 0.76 to 0.80, with
further additions raising it to 0.82.

It is important to highlight that this figure averages results from 17
different datasets, using only the Random Forest classifier. Detailed results
for each individual task are available in Figure 4 in Appendix B. The use of a
single classifier in this analysis was a deliberate choice to isolate the
impact of increasing the number of features, thereby minimizing the influence
of classifier selection on the results. However, this choice may also limit
the generalizability of the findings, as it differs from previous analyses
where the optimal classifier was selected for each task.

Figure 3: Sensitivity Analysis of Feature Count on $\mu F1$ Score. The figure
illustrates the effect of increasing the number of features (secondary
questions) on the $\mu F1$ score across 17 datasets. The solid orange line
represents the average $\mu F1$ score, and the shaded area indicates the first
standard deviation from the mean across 100 repetitions. The graph
demonstrates a consistent improvement in $\mu F1$ as more features are added,
with key points of increase highlighted at specific feature counts.

##  7 Discussion

Our study introduces the Interpretable Cross-Examination Technique (ICE-T), a
novel prompting method that integrates LLM responses with traditional
classification algorithms to improve the performance on binary classification
tasks. This technique addresses key limitations in zero-shot and few-shot
learning by employing a structured, multi-prompt approach that transforms
qualitative data into quantifiable metrics, thus allowing a small, traditional
classifier to effectively make decisions. Our results confirm that ICE-T
consistently surpasses zero-shot baselines across multiple datasets and
metrics, particularly in scenarios where model interpretability is crucial.
This prompting strategy also demonstrates the potential for fully automated,
high-performing AI systems accessible even to non-experts.

The ICE-T method has demonstrated its capability to not only enhance
performance over the zero-shot approach but also to do so with smaller models
that might not perform as well in a zero-shot configuration. For example, the
improvement in the CREATININE and ENGLISH tasks within clinical trials data
underscores the method’s ability to handle domain-specific challenges that
require nuanced understanding, which zero-shot configurations typically
struggle with.

###  7.1 Implications for Model Interpretability

A major advantage of the ICE-T approach is its interpretability. By generating
a feature vector based on direct responses to structured prompts, experts can
trace back the decision-making process, understanding which factors
contributed most significantly to the model’s classification. This is
particularly valuable in fields like medicine and law, where decision
rationale is as important as accuracy. The ability to dissect and validate
each step of the model’s reasoning aligns with the growing demand for
transparency in AI applications, ensuring that decisions made by AI systems
can be audited and trusted.

Moreover, ICE-T is particularly valuable in situations where fine-tuning
models is not viable. Fine-tuned models often suffer from a significant
drawback: they lack transparency and become “black boxes,” making their
decision-making processes obscure. This lack of interpretability is
particularly problematic in regulated sectors such as healthcare, law and
finance, where it’s imperative to comprehend the basis of each decision. ICE-T
overcomes these issues by employing a methodology that remains clear and
interpretable, avoiding the opaqueness associated with fine-tuned systems.

###  7.2 Limitations and Future Work

Despite its strengths, the ICE-T method has some limitations. The quality of
the output heavily relies on the initial set of questions generated for the
model to answer. Poorly formulated questions or those that fail to capture the
necessary subtleties of the task can limit the effectiveness of this
technique. Moreover, the reliance on numerical scoring of textual answers
might oversimplify complex answers.This can lead to a loss of nuance,
especially when answers are confined to binary outputs.

Future research could explore more sophisticated methods for question
generation, perhaps incorporating active learning where the system identifies
and prioritizes questions that would most improve its understanding and
performance. Additionally, exploring different methods of encoding responses
into feature vectors could further enhance the model’s accuracy and
sensitivity to nuances in text.

Expanding the scope of ICE-T to tackle problems beyond binary classification
could also prove beneficial. Applying this method to multi-class
classification tasks or even regression problems could test the adaptability
and scalability of the approach, potentially making it more applicable across
a wider array of domains. This expansion could lead to significant
advancements in the field of machine learning where interpretability and
accuracy are crucial.

In conclusion, the ICE-T method presents a promising avenue for enhancing the
performance and interpretability of LLMs in binary classification tasks and
beyond. By bridging the gap between traditional machine learning techniques
and modern LLM capabilities, this approach offers a valuable tool for
applications demanding high accuracy and clear reasoning in decision-making
processes. Further refinements and adaptations of this technique could
significantly impact the deployment of AI in critical sectors, enhancing both
the reliability and accountability of automated systems.

## Reproducibility

The experiment is composed of two primary phases: 1) collecting outputs from
OpenAI’s ChatGPT models, specifically using either `gpt-4-0125-preview` or
`gpt-3.5-turbo-0125`; and 2) verbalizing the answers (converting the responses
into numerical form), training classifiers and evaluating their performance on
a hold-out test set.

The code to reproduce the verbalization, classfier training and testing is
available on GitHub: https://github.com/gmuric/ICE-T

Due to data usage and confidentiality constraints associated with the clinical
trial dataset, we are unable to share the complete working code for the first
phase. However, we provide the outputs of the LLMs we obtained. They are
available in GitHub repository. We additionally provide pseudo-code that
illustrates the extraction of outputs from the language models that can be
used to reproduce the first part of the experiment. The complete references to
the data used in the experiments are explained in Section 4. Additionally, we
include a comprehensive list of the questions used to prompt the language
models in Appendix A. The pseudo-code for obtaining the answers from the LLM
is presented below:

Algorithm 1 Collecting outputs from LLM

for each textual document $t$ do

Prompt LLM with $t$ and corresponding questions {Refer to Section 3.2}

for each question $q_{i}$ related to $t$ do

$a_{i}\leftarrow$ Output of LLM for $q_{i}$

end for

$A\leftarrow$ Set of all outputs $\\{a_{1},a_{2},\ldots,a_{n}\\}$

for each output $a_{i}$ in $A$ do

$v_{i}\leftarrow$ Numerical value of $a_{i}$ {Refer to Section 3.3}

end for

$V_{d}\leftarrow$ Feature vector of all $v_{i}$

end for

Note that due to the stochastic nature of large language models, the outputs
may vary with each experiment. While these variations are unlikely to
significantly impact the results, minor discrepancies are possible.

## Acknowledgment

This material is based upon work supported by the Army ASA(ALT) SBIR CCOE
under Contract No. W51701-22-C-0035 and the US Air Force under Contract No.
FA8750-22-C-0511. Any opinions, findings and conclusions or recommendations
expressed in this material are those of the author(s) and do not necessarily
reflect the views of the Army ASA(ALT) SBIR CCOE or the US Air Force.

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##  Appendix A Questions used in ICE-T method

This is the list of questions used for each task in ICE-T technique. For each
task, there are five questions in total, where the question with $id=0$ is
used in a prompt in a zero-shot approach. Other questions are secondary
questions obtained as explained in Section 3.1:

#### DRUG-ABUSE

  1. 0

Is there any indication in the patient’s records of current or past drug
abuse?

  2. 1

Has the patient been prescribed medication with a high potential for abuse?

  3. 2

Are there any notes in the patient’s records indicating substance abuse or
dependency issues?

  4. 3

Has the patient previously sought treatment for substance abuse or addiction?

  5. 4

Are there any irregularities in the patient’s prescription history that
suggest misuse, such as early refill requests?

#### ALCOHOL-ABUSE

  1. 0

Has the patient consumed alcohol beyond the weekly recommended limits
recently?

  2. 1

Did the patient consume more than 14 units of alcohol in the past week?

  3. 2

Has the patient engaged in binge drinking sessions in the last month?

  4. 3

Does the patient frequently consume alcohol on more than 5 days in a week?

  5. 4

Has the patient expressed concerns about controlling their alcohol intake
recently?

#### ENGLISH

  1. 0

Does the patient speak English, according to their medical records?

  2. 1

Is there a language preference indicated in the patient’s medical records?

  3. 2

Does the medical record include an interpreter request for non-English
languages?

  4. 3

Is English listed as the patient’s primary language in the medical records?

  5. 4

Have previous medical consultations been conducted in English, as noted in the
patient’s records?

#### MAKES-DECISIONS

  1. 0

Is there evidence that the patient makes their own medical decisions?

  2. 1

Does the patient have a documented history of discussing treatment options
with their healthcare provider?

  3. 2

Has the patient previously filled out an advanced directive or a living will?

  4. 3

Does the patient regularly attend medical appointments alone?

  5. 4

Have there been instances where the patient explicitly expressed their
treatment preferences or declined certain medical interventions?

#### ABDOMINAL

  1. 0

Is there a record of the patient undergoing intra-abdominal surgery, small or
large intestine resection, or experiencing a small bowel obstruction?

  2. 1

Has the patient ever undergone any form of intra-abdominal surgery?

  3. 2

Has the patient had a resection of either the small or large intestine?

  4. 3

Is there a record of the patient experiencing a small bowel obstruction?

  5. 4

Has the patient had any surgical intervention related to the digestive system
that is not explicitly mentioned above?

#### MAJOR-DIABETES

  1. 0

Does the patient have any major diabetes-related complications such as
amputation, kidney damage, skin conditions, retinopathy, nephropathy, or
neuropathy?

  2. 1

Has the patient experienced any significant changes in their vision or been
diagnosed with retinopathy?

  3. 2

Is there any history of skin conditions, wounds that heal poorly, or any
amputations?

  4. 3

Does the patient have a history of kidney damage or been diagnosed with
nephropathy?

  5. 4

Has the patient reported any persistent numbness, pain, or tingling in their
extremities indicating neuropathy?

#### ADVANCED-CAD

  1. 0

Is the patient currently taking two or more medications to treat CAD?

  2. 1

Is the patient currently being treated for coronary artery disease (CAD)?

  3. 2

Is the patient taking any medication to manage CAD symptoms?

  4. 3

Is the patient prescribed more than one medication specifically for CAD?

  5. 4

Are the medications the patient is taking to treat CAD being taken
concurrently?

#### MI-6MOS

  1. 0

Has the patient had a myocardial infarction within the past 6 months from the
most recent record date?

  2. 1

Has the patient reported any chest pain or symptoms consistent with a
myocardial infarction in the past 6 months?

  3. 2

Has the patient undergone any cardiac diagnostic tests, such as an ECG or
troponin levels, in the past 6 months?

  4. 3

Did the patient receive any treatment specifically for myocardial infarction,
such as medication, stenting, or bypass surgery, in the past 6 months?

  5. 4

Is there any notation in the patient’s medical records of a confirmed
diagnosis of myocardial infarction within the past 6 months?

#### DIETSUPP-2MOS

  1. 0

Has the patient taken any dietary supplements, excluding Vitamin D, in the
past 2 months?

  2. 1

Have you taken any vitamins or dietary supplements in the past 2 months,
besides Vitamin D?

  3. 2

Are there any non-prescription supplements you have consumed regularly in the
last 60 days?

  4. 3

Did you start or continue taking any herbal or nutritional supplements
recently, except Vitamin D?

  5. 4

Excluding Vitamin D, have you used any health or wellness supplements since
two months ago?

#### ASP-FOR-MI

  1. 0

Is the patient using aspirin to prevent myocardial infarction based on their
medical records?

  2. 1

Is there a history of cardiovascular disease in the patient’s medical records?

  3. 2

Has the patient been prescribed aspirin for long-term use?

  4. 3

Do the medical records indicate a doctor’s recommendation for aspirin to
prevent myocardial infarction?

  5. 4

Is there any mention of aspirin under the patient’s current medications list?

#### HBA1C

  1. 0

Have any HbA1c test results been listed in the records with a value between
6.5 and 9.5

  2. 1

Are there any HbA1c test results listed in the records?

  3. 2

Do any HbA1c test results fall within the 6.5 to 9.5

  4. 3

Is the HbA1c value specifically mentioned for each test result?

  5. 4

Have all the HbA1c test results been recorded and updated in the records?

#### CREATININE

  1. 0

Is there any indication in the patient’s records of serum creatinine levels
above the upper limit of normal?

  2. 1

Has the patient undergone any recent serum creatinine tests?

  3. 2

Are the results of the patient’s serum creatinine tests documented in their
medical records?

  4. 3

Do the documented serum creatinine levels exceed the established normal range?

  5. 4

Has there been any physician commentary or notes indicating concern over the
patient’s serum creatinine levels?

#### UNFAIR-ToS

  1. 0

Does this sentence from company’s Terms of Service violate the European
Council Directive on unfair terms in consumer contracts?

  2. 1

Does the sentence create a significant imbalance between the parties’ rights
and obligations to the detriment of the consumer?

  3. 2

Is the term clearly understandable and transparent to a typical consumer?

  4. 3

Does the term determine the main subject matter of the contract, or the
appropriateness of the price or remuneration, in a way that is unfair to the
consumer?

  5. 4

Has the consumer been given an opportunity to negotiate the terms of the
contract?

#### ECtHR

  1. 0

Considering all the facts, did a state breach human rights provisions of the
European Convention of Human Rights?

  2. 1

Did the state fail to provide adequate protection to an individual or group,
thereby violating Article 2 or 3 concerning the right to life and prohibition
of torture?

  3. 2

Was there any discrimination in the state’s actions or laws that contravenes
Article 14 or Protocol 12 regarding the prohibition of discrimination?

  4. 3

Did the state unlawfully interfere with privacy, family life, freedom of
expression, or freedom of assembly and association, as protected under
Articles 8 to 11?

  5. 4

Was there a failure by the state to ensure a fair trial or access to a court,
in violation of Article 6 or 13, concerning the right to a fair trial and the
right to an effective remedy?

#### Climate detection

  1. 0

Is a given paragraph climate-related?

  2. 1

Does the paragraph discuss weather, temperature trends, or climate patterns?

  3. 2

Does the paragraph mention the impact of human activity on the environment?

  4. 3

Does the paragraph reference scientific studies or data on climate change?

  5. 4

Does the paragraph address policy or actions taken to mitigate climate
impacts?

#### Health advice

  1. 0

Does the given sentence contain a medical advice?

  2. 1

Does the sentence recommend or suggest a specific medication, treatment, or
remedy?

  3. 2

Does the sentence advise on health behaviors, such as diet, exercise, or sleep
patterns?

  4. 3

Does the sentence include any wording that implies or directly states a
medical diagnosis or prognosis?

  5. 4

Does the sentence suggest consulting a healthcare professional or seeking
medical attention?

#### Catalonia indep.

  1. 0

Does the given sentence speak in favor of Catalonia independence?

  2. 1

Does the sentence express a positive opinion about Catalonia’s political
autonomy?

  3. 2

Does the sentence criticize the Spanish government’s policies towards
Catalonia?

  4. 3

Does the sentence highlight benefits or advantages of Catalonia being
independent?

  5. 4

Does the sentence encourage actions or steps towards achieving independence
for Catalonia?

##  Appendix B Additional results

This is the appendix section where additional results are provided.

| $\mu F1$ | $MF1$ | $wF1$ | Sample size
---|---|---|---|---
| 0-shot | ICE-T | 0-shot | ICE-T | 0-shot | ICE-T | Train | Test
ABDOMINAL | 0.791 | 0.814 | 0.740 | 0.774 | 0.775 | 0.803 | 202 | 86
ADVANCED-CAD | 0.640 | 0.756 | 0.593 | 0.743 | 0.600 | 0.746 | 202 | 86
ALCOHOL-ABUSE | 0.814 | 0.965 | 0.583 | 0.491 | 0.872 | 0.948 | 202 | 86
ASP-FOR-MI | 0.849 | 0.86 | 0.73 | 0.728 | 0.834 | 0.838 | 202 | 86
CREATININE | 0.349 | 0.721 | 0.319 | 0.419 | 0.256 | 0.604 | 202 | 86
DIETSUPP-2MOS | 0.593 | 0.767 | 0.562 | 0.765 | 0.564 | 0.766 | 202 | 86
DRUG-ABUSE | 0.942 | 0.977 | 0.757 | 0.827 | 0.954 | 0.977 | 202 | 86
ENGLISH | 0.919 | 0.988 | 0.810 | 0.978 | 0.910 | 0.989 | 202 | 86
HBA1C | 0.477 | 0.837 | 0.410 | 0.834 | 0.373 | 0.838 | 202 | 86
MAJOR-DIABETES | 0.733 | 0.814 | 0.728 | 0.814 | 0.728 | 0.814 | 202 | 86
MAKES-DECISIONS | 0.605 | 0.965 | 0.426 | 0.491 | 0.724 | 0.948 | 202 | 86
MI-6MOS | 0.651 | 0.919 | 0.542 | 0.709 | 0.724 | 0.91 | 202 | 86
Catalonia indep. | 0.528 | 0.579 | 0.414 | 0.536 | 0.413 | 0.536 | 2961 | 1145
Climate detection | 0.702 | 0.8 | 0.673 | 0.444 | 0.732 | 0.711 | 1300 | 400
ECtHR | 0.853 | 0.873 | 0.65 | 0.466 | 0.849 | 0.814 | 2384 | 591
Health advice | 0.836 | 0.841 | 0.778 | 0.787 | 0.830 | 0.835 | 3470 | 868
UNFAIR-ToS | 0.335 | 0.887 | 0.321 | 0.47 | 0.397 | 0.834 | 3319 | 964
$\mu F1$ \- Micro F1, $MF1$ \- Macro F1, $wF1$ \- Weighted F1
Table 2: Evaluation of ICE-T and Zero-Shot Techniques on GPT-3.5 - This table presents a comparison of F1 micro, macro, and weighted scores for various classification tasks using GPT-3.5. It compares the performance metrics between the zero-shot approach and the ICE-T technique. Additionally, the table includes sample sizes for both training and testing datasets across each task. Notable improvements can be seen in several tasks when utilizing the ICE-T technique over the zero-shot method. | $\mu F1$ | $MF1$ | $wF1$ | Sample size
---|---|---|---|---
| 0-shot | ICE-T | 0-shot | ICE-T | 0-shot | ICE-T | Train | Test
ABDOMINAL | 0.802 | 0.884 | 0.757 | 0.876 | 0.789 | 0.885 | 202 | 86
ADVANCED-CAD | 0.791 | 0.907 | 0.785 | 0.906 | 0.787 | 0.906 | 202 | 86
ALCOHOL-ABUSE | 0.791 | 0.965 | 0.564 | 0.691 | 0.856 | 0.962 | 202 | 86
ASP-FOR-MI | 0.860 | 0.895 | 0.770 | 0.802 | 0.854 | 0.881 | 202 | 86
CREATININE | 0.488 | 0.872 | 0.487 | 0.85 | 0.477 | 0.875 | 202 | 86
DIETSUPP-2MOS | 0.488 | 0.814 | 0.328 | 0.814 | 0.336 | 0.814 | 202 | 86
DRUG-ABUSE | 0.826 | 0.977 | 0.593 | 0.744 | 0.879 | 0.971 | 202 | 86
ENGLISH | 0.233 | 0.965 | 0.231 | 0.925 | 0.206 | 0.963 | 202 | 86
HBA1C | 0.523 | 0.942 | 0.479 | 0.941 | 0.451 | 0.942 | 202 | 86
MAJOR-DIABETES | 0.570 | 0.884 | 0.523 | 0.884 | 0.523 | 0.884 | 202 | 86
MAKES-DECISIONS | 0.663 | 0.965 | 0.456 | 0.691 | 0.768 | 0.962 | 202 | 86
MI-6MOS | 0.849 | 0.953 | 0.674 | 0.844 | 0.868 | 0.95 | 202 | 86
Catalonia indep. | 0.562 | 0.604 | 0.462 | 0.57 | 0.463 | 0.57 | 2961 | 1145
Climate detection | 0.912 | 0.925 | 0.878 | 0.896 | 0.917 | 0.929 | 1300 | 400
ECtHR | 0.861 | 0.873 | 0.631 | 0.466 | 0.848 | 0.814 | 2384 | 591
Health advice | 0.846 | 0.846 | 0.766 | 0.766 | 0.828 | 0.828 | 3470 | 868
Unfair TOS | 0.837 | 0.889 | 0.63 | 0.489 | 0.844 | 0.839 | 3319 | 964
$\mu F1$ \- Micro F1, $MF1$ \- Macro F1, $wF1$ \- Weighted F1
Table 3: Evaluation of ICE-T and Zero-Shot Techniques on GPT-4 - This table presents a comparison of F1 micro, macro, and weighted scores for various classification tasks using GPT-4. It compares the performance metrics between the zero-shot approach and the ICE-T technique. Additionally, the table includes sample sizes for both training and testing datasets across each task. Notable improvements can be seen in several tasks when utilizing the ICE-T technique over the zero-shot method. |  | $\mu F1$ | $MF1$
---|---|---|---
|  | 0-shot | ICE-T | 0-shot | ICE-T
Clinical Trial | GPT3.5 | 0.709 | 0.866 | 0.604 | 0.695
| GPT4 | 0.673 | 0.915 | 0.560 | 0.803
Other | GPT3.5 | 0.600 | 0.777 | 0.546 | 0.559
| GPT4 | 0.789 | 0.816 | 0.684 | 0.680
Table 4: Comparison of average performance between Zero-Shot and ICE-T methods
across Clinical Trial and Other Task Group Figure 4: Task-Specific Sensitivity
Analysis of Feature Count on $\mu F1$ Score. Detailed view of the changes in
the $\mu F1$ score for individual tasks as the number of secondary questions
increases. Each plot represents one of the 17 datasets analyzed, showing how
the micro F1 score varies with the addition of features. The data underscores
the variability in performance improvements across different tasks when using
the Random Forest classifier.