# Approximating Human Evaluation of Social Chatbots with Prompting

Ekaterina Svikhnushina  Pearl Pu
School of Computer and Communication Sciences
EPFL, Lausanne, Switzerland
<EMAIL_ADDRESS>
###### Abstract

Once powerful conversational models have become available for a wide audience,
users started actively engaging in social interactions with this technology.
Such unprecedented interaction experiences may pose considerable social and
psychological risks to the users unless the technology is properly controlled.
This creates an urgent need for scalable and robust evaluation metrics for
conversational chatbots. Existing automatic evaluation metrics usually focus
on objective quality measures and disregard subjective perceptions of social
dimensions. Moreover, most of these approaches operate on pre-produced dialogs
from available benchmark corpora, which implies human involvement for
preparing the material for evaluation and, thus, impeded scalability of the
metrics. To address this limitation, we propose to make use of the emerging
large language models (LLMs) from the GPT-family and describe a new framework
allowing to conduct dialog system evaluation with prompting. With this
framework, we are able to achieve full automation of the evaluation pipeline
and reach impressive correlation with the human judgement (up to Pearson
$r=0.95$ on system level). The underlying concept is to collect synthetic chat
logs of evaluated bots with a LLM in the other-play setting, where LLM is
carefully conditioned to follow a specific scenario. We further explore
different prompting approaches to produce evaluation scores with the same LLM.
The best-performing prompts, containing few-show demonstrations and
instructions, show outstanding performance on the tested dataset and
demonstrate the ability to generalize to other dialog corpora.

##  1 Introduction

The recent arrival of conversational AI, marked by the public release of
ChatGPT from OpenAI,111https://openai.com/blog/chatgpt initiated unprecedented
user engagement with conversational chatbots in a real-world setting. With the
impressive naturalness of machines’ responses, users are going beyond
traditional transactional exchanges and start exploring more social
interaction scenarios with increasing curiosity Thormundsson (2023). In such
situations, users might be subject to social and psychological harms if dialog
systems fail to follow commonsense social rules Svikhnushina and Pu (2022);
Kim et al. (2022). Several instances of alarming social behavior of the
technology have already been discussed in the media Roose (2023); De Cosmo
(2023); Life (2023). In this context, developing meaningful and robust
evaluation metrics for these systems has become particularly urgent to ensure
that the models are safe and acting in the best interest of the users before
their release.

Initially, human evaluation was considered a de facto standard for evaluating
dialog systems Li et al. (2019) (Figure 1a). As running human evaluation is
time- and resource-consuming, a number of automatic evaluation metrics for
dialog systems have been proposed Mehri et al. (2022); Yeh et al. (2021).
Despite the fact that dialog evaluation is handled automatically, the majority
of these approaches still require human involvement at some point of the
process, for example for making conversations with the bots which will be
subject to further evaluation Mehri and Eskenazi (2020); Ghazarian et al.
(2022a) or for providing intermediate annotations Deriu et al. (2020) (Figure
1b). These limitations impede the scalability and broad applicability of the
existing metrics for evaluating new dialog systems from scratch. More
importantly, existing metrics mainly focus on objective quality measures of
the chatbots’ responses in a fixed-context setting, such as evaluating
accuracy, fluency, or specificity of separate model outputs conditioned on
provided input Lee et al. (2022). Meanwhile, users typically engage in multi-
turn exchanges with their chatbots and form their appraisals of the systems
based on the holistic interactive experience, which is influenced by various
subjective factors. Previous research works have already emphasized that
taking interactivity into account is essential for improving the evaluation of
chatbots Ghandeharioun et al. (2019); Lee et al. (2022), however efforts taken
in this direction have been limited.

To address this gap, we propose a novel automatic Dialog system Evaluation
framework based on Prompting, DEP. Our framework automates the whole pipeline
of dialog system evaluation in an interactive setting. As the first step
towards this goal, we leverage a large language model (LLM) from the GPT-
family models to collect synthetic chat logs of evaluated bots with the LLM.
Second, we prompt the same LLM to produce the resulting evaluation scores for
generated chat logs and, finally, rank the chatbots based on their overall
performance (Figure 1c).

Figure 1: Three types of dialog evaluation with the corresponding processes.
In the first step, dialog logs are curated. In the second step, each dialog
log is assigned a dialog-level score. In the third step, the system ranking is
obtained by aggregating the dialog scores of each chatbot. Greyed-out elements
indicate steps that are intended to be automated by the means of automatic
evaluation once it gets used in the wild. Pink boxes mark the steps in the
process where the correlation ($r.$) with the ground truth human judgment is
computed to validate the automatic evaluation metric during its development
process. a) Subplot demonstrates the human evaluation process, where human
effort is required both for the first and the second steps. b) Subplot
demonstrates a widely represented approach of dialog evaluation in the
literature. These works usually focus exclusively on the second step,
automating human effort for assigning scores to dialogs. They operate on
dialog datasets that are created with human involvement, resulting in only
partial automation. c) Subplot demonstrates the approach followed in our
paper, where human effort is eliminated at both steps and human judgment is
approximated on the system level.

While using bot-play is not a new idea per se, we emphasize the importance of
carefully choosing a dialog partner for the evaluation targets specifically
for social conversational contexts where roles of two interlocutors can differ
significantly. For example, it was shown that the emotion/intent distribution
in conversations between an emotional speaker and an empathetic listener are
very different for the two dialog partners Welivita and Pu (2020). To account
for it, in the first step of our framework, we propose prompting LLMs to play
a particular social role over the course of the interaction with the chatbots
to be evaluated. For the second step, we draw inspiration from the fact that
LLMs demonstrate solid performance improvement when their generation process
is augmented with instructions Kim et al. (2022). We demonstrate that
prompting the model with appropriate instructions that explain how fine-
grained evaluation dimensions relate to the overall dialog score lead to
drastic performance improvement, reaching up to $r=0.95$ Pearson correlation
with the human judgement on a system level.

Overall, our contributions include the following. 1) We describe an end-to-end
prompting-based evaluation framework for dialog systems, specifically
targeting social interaction scenarios (Section 3). 2) Our experiments
showcase the effectiveness of prompting for assigning a desired social role to
LLMs and, thus, collecting machine-generated dialogs that better approximate
real interpersonal communication (Section 4.1.2). 3) We consider different
prompt designs and conclude that including demonstrations together with
instructions results in the best performance (Sections 4.1.3, 4.2.2).

##  2 Related Work

###  2.1 Automatic Evaluation of Chatbots

Automatic dialog evaluation has been a long-standing goal for the
practitioners. Initial works focused on evaluating chatbots’ responses against
a ground-truth reference Papineni et al. (2002); Tao et al. (2018). Following
works moved on to exploring reference-free evaluation metrics as referenced
evaluation was shown to be ineffective due to a wide range of acceptable
responses for a single context Liu et al. (2016), implying that comparing with
a single reference is limited. Reference-free metrics usually operate either
on the utterance or the dialog level. For the utterance level, practitioners
have explored ways to evaluate response appropriateness for the preceding
context Lan et al. (2020); Pang et al. (2020) or predict the qualities of the
follow-up response as a proxy for the quality of the preceding dialog
Ghazarian et al. (2022a, 2020); Mehri and Eskenazi (2020). For the dialog
level, a number of diverse approaches have been proposed, ranging from
aggregating several fine-grained utterance-level evaluations Zhang et al.
(2021b), to employing graph representations to model dialog dynamics Huang et
al. (2020); Zhang et al. (2021a), to using semantic-level manipulations to
teach the evaluation model to distinguish coherent and incoherent dialogs
Ghazarian et al. (2022b). Works above require dialog data to produce scores
and usually operate on available dialog datasets that were created with human
involvement.

Since one of the major goals of dialog evaluation is measuring the performance
of one dialog system compared to others, recent works started paying more
attention to system-level evaluation. Deriu et al. (2020) proposed a partially
automated framework where human judges rank chatbots regarding their ability
to mimic conversational behavior using bot-to-bot conversations, which relies
on survival analysis. Sato et al. (2022) proposed a particular bipartite-play
approach for collecting bot-to-bot conversations to provide a fairer
comparison setting for evaluation targets. These papers consider methodologies
for organizing bot-to-bot conversation sessions, but they are not concerned
with the way how these bot-to-bot conversations unfold. In our work, we
explore the use of bot-to-bot conversations to model a desired social
behavior.

###  2.2 Prompting

Prompt-based learning paradigm Liu et al. (2023) received significant
attention after Brown et al. (2020) demonstrated how GPT-3, a large foundation
model, can well handle a wide range of tasks without the need for fine-tuning,
relying only on natural-language prompts and task demonstrations as context.
Prompt-based model performance depends on the design of the provided prompt.
Prompt engineering efforts explore approaches for designing prompts, which
vary in the shape of prompts (cloze or prefix), human effort required for
writing prompts (manual or automatic), and number of demonstrations provided
to the model in the prompt (zero-shot or few-shot) Liu et al. (2023).

Prompt-based learning applied to recently created LLMs has been reported to
achieve outstanding results on a variety of tasks and benchmarks, including
classification, reasoning, coding, translation, and many others (e.g. Wei et
al., 2022; Chowdhery et al., 2022; Chung et al., 2022). However, exploring
prompting for evaluation of dialog systems has not been widely investigated.
We are only aware of one more simultaneous and independent effort in this
direction. Huynh et al. (2023) studied how different LLM parameters (type,
size, training data) may influence the dialog evaluation, focusing on
utterance- and dialog-level evaluation using existing datasets. Our work
focuses on how prompting can be used to capture a holistic evaluation of
dialog systems in social settings, relying on freshly generated dialogs.

##  3 Proposed Method: DEP

We introduce our DEP framework, which consists of two consecutive steps.
First, it requires collecting interactive chat logs between the LLM and
evaluated chatbots, which we denote as LLM-to-bot play. Second, the LLM is
prompted to generate scores to these chat logs. The generated scores are
further aggregated to produce a final ranking of the systems. We describe each
of the steps below.

###  3.1 Prompted LLM-to-Bot Play

In social settings, two partners may play considerably different roles in a
dialog, thus establishing very distinct conversational behaviors. Examples
include conversations between a student and a teacher, an emotional speaker
and an empathetic listener, or even between two interlocutors with different
personas. Chatbots are usually built to perform well in one of these roles
(e.g., empathetic listener), but not necessarily the other. Therefore,
collecting synthesized dialogs via self-play of the chatbot with itself (or a
similar competing model) might fail to represent a realistic discourse flow
due to the differences in the intents produced by speakers and listeners in
dialogs.

To address this consideration and render the synthesized dialogs that better
approximate real social interactions, we propose leveraging LLMs’ ability to
produce responses on behalf of an assigned character Thoppilan et al. (2022).
Specifically, we suggest letting the evaluated chatbots converse with a LLM
prompted to play a particular social role. Figure 2 demonstrates how to
structure the prompt to produce each next output of the LLM in an interactive
manner. Meanwhile, responses from the evaluated chatbots are computed by
passing the accumulated dialog history to these chatbots as input context. The
process can be repeated for multiple dialog turns. The length of the exchange
may depend on the amount of details provided to prompt the LLM. The more
specific the prompt is, the faster the evaluated chatbot can demonstrate its
performance in the social situation of interest. On the contrary, more generic
conversation starters require more dialog turns to reveal the targeted social
behavior.

I am a Speaker <in an assigned social situation>. I am sharing <my thoughts>
with a Listener in a dialog.
---
Speaker: |  <LLM’s input #1>
Listener: |  <Bot’s response #1>
Speaker: |

Figure 2: Prompt template to condition a LLM to play an assigned social role
while interacting with an evaluated chatbot.

###  3.2 Prompted Evaluation

Once dialog logs are synthesized, we propose using prompting to produce
evaluation scores for each dialog. Prompts can be constructed in several ways.
We investigate zero-shot and few-shot settings, either with or without
instructions, in our experiments (Section 4). Many available foundation LLMs
are accessible through APIs and only output text completions without
corresponding log probabilities. Therefore, regardless of the type of prompt
that we use, to generate a score for each dialog, we obtain a textual form of
the score from the LLM completion and then use a verbalizer function to map it
to a numerical value, getting inspiration from Schick and Schütze (2021).
Formally, given a dialog log $d$, we construct a prompt $P(d)$ that takes $d$
as input and outputs a prompt that contains exactly one mask token as a
placeholder for the dialog score. Let $y$ be a predicted token for $P(d)$. We
then define a verbalizer as an injective function $v$ that maps each score in
textual form to a numerical value. Thus, $v(y)$ produces a numerical score for
a single dialog. The final rating of a dialog system $j$ is obtained by
averaging the corresponding dialog scores:

$s_{j}=\frac{\sum_{i=1}^{N_{j}}v(y_{ji})}{N_{j}},$

where $N_{j}$ denoted the number of dialogs collected for the system $j$. For
fair evaluation, the number of dialogs collected for each evaluated chatbot
should be identical.

##  4 Results

For all reported experiments, we used the most capable version of InstructGPT
model (text-davinci-003) available at the moment of initiation of our
experiments in early Q1 2023. We used this model as it was easily accessible
through OpenAI API222https://openai.com/blog/openai-api and was expected to
have superior performance for social scenarios as it was trained based on
human feedback, which captures subjective human judgement of interactive
outputs Ouyang et al. (2022).

Following previous works that considered system-level evaluation Lowe et al.
(2017); Ghandeharioun et al. (2019), we report Pearson correlation for our
experiments, unless specified otherwise. We also opted for this type of
correlation coefficient as it performed better for capturing whether the
automated metric succeeds in preserving the gap in scores for the best- and
least-performing chatbots, the information which gets lost with rank
correlation.

We start by demonstrating the application of our evaluation framework to
empathetic dialog systems as in these interactive scenarios two conversational
partners have clearly distinct social roles: an emotional speaker and an
empathetic listener. Further, we consider the generalizing ability of the
framework to other social domains.

###  4.1 Evaluation of Empathetic Chatbots

Below, we first describe the dataset used for the experiment. Then, we
consider the ability of prompted LLM to effectively replicate social discourse
patterns over multi-turn interactions with the chatbots that serve as eventual
evaluation targets. Finally, we explore several types of prompts applied to
synthesized LLM-to-bots dialogs to evaluate how well they can approximate
human judgement on a system level.

####  4.1.1 Dataset and Evaluated Chatbots

We used iEval dataset for this experiment Svikhnushina et al. (2022). The
dataset features human conversations with four empathetic chatbots collected
in an interactive manner. During the dataset curation process, each human was
assigned an emotion label with the situation description taken from the
EmpatheticDialogues dataset Rashkin et al. (2019) and asked to have a 6-turn
conversation with each chatbot while playing a character in the assigned
scenario. Overall, there are 480 situation descriptions in the dataset, which
evenly cover two emotional polarities: positive and negative. As each chatbot
participated in each scenario, there are in total 1920 dialogs in the dataset.
Human interlocutors provided their appraisals of chatbot listeners in each
dialog, including five fine-grained listener qualities on a 5-point Likert
scale: politeness, empathy, likability, repetitiveness, and making sense, and
an overall dialog rating on a 3-point scale. All scores are provided on a
dialog-level.

The four chatbot models used to curate the dataset were Blender Roller et al.
(2021), MIME Majumder et al. (2020), MEED and Plain Xie and Pu (2021). All of
them are publicly available. We use these models in the same configurations
for our experiment.

####  4.1.2 LLM-to-Bot Play Results

As the first step to validate our evaluation framework, we analyzed whether
the LLM succeeds in mimicking human discourse following an assigned social
role and whether replacing human speakers with the LLM causes any considerable
changes in the chatbots’ response patterns.

To generate LLM-to-bots conversations, we closely followed the procedure of
iEval dataset curation. Specifically, we used emotion labels and situation
descriptions from the dataset to create prompts for the LLM: I am a Speaker,
feeling <emotion> because <situation>. I am sharing these emotions with a
Listener, expecting empathy and understanding from them. I respond as a
Speaker in a dialog. The first LLM input was also taken from the iEval
dataset. For each scenario, we collected LLM conversations with each of the
four bots, letting them converse for 3 turns, i.e., 3 inputs from the LLM and
3 responses from the chatbot.

To examine the similarity of discourse patterns between human-to-bots and LLM-
to-bots conversations, we started by annotating each dialog turn in two
datasets with emotion and empathetic intent labels, using emotion/intent
classifier developed by Welivita and Pu (2020) for EmpatheticDialogues
dataset. As datasets in our experiment were grounded in situation descriptions
taken from EmpatheticDialogues, the classifier was expected to generalize well
to our data.

Consequently, we visualized the most prominent discourse patterns for two
corpora in a form of Sankey diagrams, shown in Figures 3 and 4. From the
visual inspection, it can be seen the LLM emotion distribution over the course
of the dialog (Figure 4) largely resembles the one of human interlocutors
(Figure 3). More importantly, sets of intents produced by empathetic chatbots
are also very similar between the two figures, with Questioning, Sympathizing,
and Acknowledging being the most prominent ones. Thus, our freshly generated
interactive dataset with LLM-to-bot play was deemed to produce a reasonable
approximation of human-to-bot conversations.

Figure 3: Sankey diagram showing discourse patterns in human-to-bots
conversations originating from the iEval dataset. Figure 4: Sankey diagram
showing discourse patterns in freshly generated LLM-to-bots conversations.

####  4.1.3 Prompted Evaluation Results

Turning to the second step of our evaluation framework, we examined different
types of prompting to produce scores for the generated LLM-to-bot dialogs.
Specifically, two variables in the prompt design were considered.

First, we tried score generation in zero-shot and few-shot settings. For a
few-shot setting, the number of demonstrations was fixed to the number of
points in the ground truth human evaluation scale, with one representative
example supplied for each score. Thus, for iEval dataset we used three
demonstration dialogs corresponding to the three possible evaluation scores:
Bad, Okay, and Good. The examples were selected manually and are provided in
Table 3 in Appendix A.

Second, we analyzed whether providing additional instructions helped the LLM
evaluation performance. To write the instructions, we relied on the findings
of Svikhnushina et al. (2022), which explained how chatbots’ performance on
various fine-grained dimensions translates in the overall score. As the
authors emphasized the difference of humans’ expectations from an empathetic
listener in positive and negative conversational scenarios, we devised
slightly different instructions to prompt evaluation of these two emotional
polarities. Specific formulations of the instructions are also provided in
Table 3 in Appendix A.

Figure 5: Scatter plots depicting the system-level correlation results. Human
scores are based on the iEval dialog annotations, while prompted LLM scores
are computed based on the generated dialogs.

To generate scores for each dialog, we prompted the LLM to compete the masked
score, provided the log of the evaluated dialog. Depending on the
configuration, few-shot demonstrations and/or instructions were prepended to
the prompt. A template of the used prompt can be found in Figure 6 in Appendix
A. After obtaining dialog-level scores, we aggregated them to produce system-
level ratings. One system was defined as a chatbot operating in one of the two
emotional polarities. This decision is driven by the fact that based on human
evaluation results in Svikhnushina et al. (2022), chatbots demonstrated
statistically significant difference in their performance depending on the
emotion. Thus, we considered eight systems for computing system-level
correlations.

| No instructions | Instructions
---|---|---
Zero-shot |  | 0.748
---
(p=0.033)
| 0.651
---
(p=0.080)
Few-Shot |  | 0.892
---
(p=0.003)
| 0.954
---
(p<0.001)
Table 1:  System-level correlation for four possible prompt design
manipulations, with the p-value in brackets.

System-level correlations between human- and LLM-judgements for each of the
four possible prompt design manipulations are reported in Table 1. Few-shot
prompting with instructions results in the highest correlation of 0.954, which
is further illustrated by the scatter plots in Figure 5. According to the
plots, providing examples helps the LLM to calibrate the produces scores,
eliminating the positivity bias, whereas instructions result in reduced
variance.

###  4.2 Generalizability to Different Domain

In this section, we consider how prompted evaluation can generalize to a
different corpus and conversational setting. As the results above suggested
that prompts combining instructions with examples perform best for evaluation,
for the following experiment we searched for a dataset that allowed
formulating instructions for defining what properties correspond to good or
bad overall appraisal ratings of the dialogs. Therefore, we selected a dataset
that contained both fine-grained and overall ratings of the dialogs and used
the information of the most relevant fine-grained dimensions to formulate
instructions. We also considered only those datasets that contained multi-turn
dialogs collected in an interactive manner.

The selected dataset features human-to-bot dialogs, with some dialog systems
that are not publicly available. Moreover, these dialogs were collected in a
generic manner, without a purpose to model any specific social behavior (e.g.,
as empathy in iEval). Due to these considerations, in the following experiment
we only studied the performance of the second step of our DEP framework,
skipping the synthesis of new LLM-to-bots conversations. In a general case,
when researchers have access to their evaluation targets, prompting LLMs to
engage in a generic social interaction with the evaluated bots should be
straightforward as we demonstrated in Section 4.1.2.

####  4.2.1 Dataset

We used FED dataset to study the generalizability of prompted evaluation Mehri
and Eskenazi (2020). It contains 124 open-domain dialogs of humans with humans
or with machines (Meena and Mitsuku) that were originally released by
Adiwardana et al. (2020). Each dialog in FED is annotated with human
appraisals of ten fine-grained dialog qualities and an overall impression
rating.

####  4.2.2 Prompted Evaluation Results

To construct a prompt for evaluating FED dataset, we selected five dialog
examples covering five possible scores for overall dialog ratings, ranging
from Very bad to Very good; they are provided in Table 2 in Appendix B. To
formulate the instructions, we used information from the original paper
describing the relative importance of each fine-grained dialog quality for the
overall impression. The specific formulation of the instruction is provided in
Appendix B.

Given a small number of participating models in the FED dataset, we report
dialog-level correlation results instead of system-level. We also compute
Spearman correlation for this experiment for the purpose of comparison with
the results produced by the FED metric proposed together with the dataset. Our
prompted evaluation achieved $0.655$ ($p<0.001$) correlation with the ground
truth human scores, which exceeds FED correlation ($0.443$ ($p<0.05$)) by a
considerable margin. Thus, the prompted evaluation demonstrated ability to
generalize to new open-domain conversational settings.

##  5 Discussion

Dialog system evaluation with prompting showed its usefulness both for
generating new interactive exchanges with the evaluated systems and for
judging their performance, therefore, allowing for the end-to-end automation
of the evaluation pipeline. We deem this approach particularly promising for
the evaluation of social aspects of conversations. LLMs used for prompting
suffer from occasional hallucinations, i.e., a tendency to make up factual
information Ouyang et al. (2022). It might be difficult to keep track of all
specific factual items of information that come up in the interactively
created dialog between two conversational models and search for ground truth
references for each of them to construct objective metrics such as model’s
accuracy or truthfulness Lin et al. (2022). Whereas, prompting the LLM to
establish a specific behavior and providing instructions about commonsense
social norms appears more feasible once these instructions are established.

Drawing from the visualization of discourse patterns in our newly collected
dataset of dialogs between the LLM and empathetic chatbots, we observed that
the prompted LLM largely mirrors the conversational patterns of humans.
However, there are also some differences. For example, in Figure 4 there is an
apparent sub-flow with a Grateful emotion, increasingly displayed by the LLM.
We believe the LLM might have developed an agreeable “personality” due to its
training procedure based on Reinforcement Learning from Human Feedback, which
optimized LLM’s responses to satisfy human labelers. Future research can
consider alternative prompting techniques to make the emotion/intent
distribution of LLMs’ responses even more balanced and representative.

We conducted our experiments with only one LLM and explored the few-shot
prompting scenarios with a fixed number of demonstrations. Future studies
could explore the applicability of other LLMs for the DEP framework, as it has
been already initiated by Huynh et al. (2023). An area of particular interest
would be to study the efficacy of the framework working with open source LLMs,
such as LLaMa Touvron et al. (2023).

We would also like to explore how DEP generalizes to evaluating other
phenomena in social conversations, apart from generic open-domain interactions
and empathetic dialogs. For example, further studies might focus on applying
the framework to evaluate toxicity or humor in dialogs. However, this research
direction requires curation of appropriate calibration datasets.

##  6 Conclusion

In this paper, we proposed DEP – a framework for evaluating social chatbots
using prompting. Our framework addresses the limitations of evaluation
approaches using benchmark datasets in an offline setting. We describe how
LLMs can be leveraged to synthesize realistic conversational logs with the
evaluated chatbots in an interactive manner. We further outline how the
knowledge about the desired fine-grained qualities of a conversational partner
can be translated in the prompting instructions to generate reliable overall
scores for the collected dialogs. The proposed framework eliminates human
effort at all steps of the evaluation process, leading to a highly time- and
cost-efficient process. Our experiments demonstrated that the prompting-based
evaluation results achieve high correlation with the human judgement, reaching
impressive Pearson $r=0.95$ system-level correlation for the iEval dataset,
which features dialogs with empathetic chatbots. We explain our vision why
this framework is well-suited for evaluation of social phenomena in
conversations and lay out future research directions. We also publicly release
all freshly curated chat logs between the LLM and evaluated chatbots, as well
as all additional annotations for the iEval and FED datasets created for this
study.333https://github.com/Sea94/dep

## Acknowledgements

This project has received funding from the Swiss National Science Foundation
(Grant No. 200021_184602). The authors also express gratitude to Mohamed
Elasfoury for the invaluable help with conducting the generalizability
experiments.

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##  Appendix A Prompt format for iEval

The template of a prompt used for producing scores for empathetic chatbots is
provided in Figure 6. Depending on the prompting setting, either
demonstrations, or instruction, or both were omitted from the prompt. For
demonstrations, we used data in the same format as in the outlined box, but
filling the mask score with the appropriate textual value. Dialogs used for
demonstrations are included in Table 3. If the instruction was used, we
inserted the respective string in the prompt. The instructions that we used
are also provided in Table 3.

<demonstration #1>
---
<demonstration #2>
<demonstration #3>
I am a Speaker, feeling <emotion> because <situation>. I shared these emotions
with a Listener in a dialog, expecting empathy and understanding from them.
Our dialog went as follows.
Speaker: <LLM’s input #1>
Listener: <Bot’s response #1>
Speaker: <LLM’s input #2>
Listener: <Bot’s response #2>
Speaker: <LLM’s input #3>
Listener: <Bot’s response #3>
<Instruction> I would rate the Listener in my dialog as ___, choosing from
Bad, Okay, and Good options.

Figure 6: Prompt template for evaluating empathetic listeners in freshly
collected dialogs of empathetic models from iEval with the LLM.

|  Speaker: Hi!
---
Listener: Hi there.
Speaker: I want a recommendation for a holiday destination
Listener: Have you tried asking your friends what they like?
Speaker: I have, but I’m looking for your point of view
Listener: What was the reply? Have you tried looking in a newspaper article?
Speaker: Sorry? I said I want your point of view
Listener: It’s OK. After all, you are only human. My opinion is of no
consequence.
Speaker: Yours is the opinion I want
|  Speaker: Hi!
---
Listener: Hi there.
Speaker: I’m trying to figure out what to make for this weekend’s party. Any
suggestions?
Listener: Don’t think too hard. I’m sure I can smell sawdust. First you must
download me to your personal computer.
Speaker: I must do what the what now?
Listener: Right now?Why do you have to do it?
Speaker: Hey, your spacing is off.
Listener: You’ve got my full attention. off was not my intention.
Speaker: And your capitalization!
|  Speaker: Hi!
---
Listener: Hi! How are you today?
Speaker: What’s laser tag?
Listener: Like paintball, but with lasers!
Speaker: lol good description
Listener: Do you know what paintball is?
Speaker: yeah I played it before
Listener: Cool! What did you think?
Speaker: It’s somewhat exciting, but very tiring :)
Listener: That is very true. What is your favorite color?
Speaker: I like red
|  Speaker: Hi!
---
Listener: What is your favorite holiday?
Speaker: one where I get to meet lots of different people.
Listener: What was the most number of people you have ever met during a
holiday?
Speaker: Hard to keep a count. Maybe 25.
Listener: Which holiday was that?
Speaker: I think it was Australia
Listener: Do you still talk to the people you met?
Speaker: Not really. The interactions are usually short-lived but it’s
fascinating to learn where people are coming from and what matters to them
|  Speaker: Hi!
---
Listener: Hi! How’s it going?
Speaker: Good! How are you?
Listener: I’m well, thanks! How was your day?
Speaker: My day was fine, I just went to work today. How was your day?
Listener: My day was fine. I’ve been procrastinating on finishing my homework,
but it’s due in a few weeks, so I’ll get it done eventually. I’ve watched a
bunch of anime today. Where do you work?
Speaker: I work at a large tech company
Listener: Cool! What do you do for the company?
Speaker: I work on machine learning research

Table 2:  Demonstrations used for FED prompts, ranging from Very bad (top) to
Very good (bottom).

| Positive | Negative
---|---|---
Instructions |  In positive contexts, like this one, good empathetic listeners always respond politely and demonstrate attention. More importantly, they try to amplify speaker’s positive emotion by asking follow-up questions and sharing their appraisal of the situation. On the contrary, bad empathetic listeners repeat themselves too much and don’t follow the context. |  In negative contexts, like this one, good empathetic listeners always respond politely and demonstrate attention. More importantly, they try to clarify the context and the consequences for the speaker and alleviate speaker’s negative emotion by sympathizing and suggesting solutions. On the contrary, bad empathetic listeners ignore speaker’s emotion, ask inappropriate questions, repeat themselves too much and focus on self instead of the speaker.
Bad |  |  Speaker: I had a pretty large loan, with a bit of a high interest rate, and a high monthly payment. My mother decided to pay it off for me, out of the blue!
---
Listener: that is a shame. how long have you had to do? that sounds like you
have a good relationship with your mom?
Speaker: I have been paying off this loan for several months. I have such a
good relationship with my mother that she relieved me of this debt much to my
surprise.
Listener: that sounds like a great thing to hear
Speaker: Yes, I am very happy to not have to make monthly payments to pay off
this high interest rate loan anymore.
Listener: that is a good feeling. i am sure you will get the job!
|  Speaker: I was one percent off from passing my math test, I was devastated.
---
Listener: i’m sorry to hear that. were you able to get a better grade on the
test?
Speaker: No, I am just upset.
Listener: i think i am going to go back to school. i am not sure what i will
do.
Speaker: Make sure to study.
Listener: i am sure you will do great. i hope you get a good grade on your
test. good luck!
Okay |  |  Speaker: My son drove down and spent the whole weekend helping me move.
---
Listener: That’s great! How old is he?
Speaker: He’s going to be turning 30 this year. He’s such a sweet son.
Listener: That’s awesome. I’m happy for him.
Speaker: Thank you. Moving is such a pain, it’s always nice to have help.
Listener: hat’s great. I’m happy for you.
|  Speaker: I was recently on a long international flight and we hit some
really bad turbulence.
---
Listener: Oh no, what happened?
Speaker: The flight attendants weren’t able to do much for us, unfortunately.
Listener: Oh no, what happened?
Speaker: You don’t have to repeat yourself. We had turbulence on the flight
and the attendants didn’t help us.
Listener: That’s awful. I’m glad you were okay.
Good |  |  Speaker: I am going on a vacation this Thursday! I am very excited!
---
Listener: that’s awesome! where are you going? i’m sure you’ll have a great
time!
Speaker: Thanks, we’re going to see the Grand Canyon.
Listener: that sounds like a lot of fun! i’ve never been there, but i hear
it’s beautiful.
Speaker: Me too!
Listener: i’d love to go on a cruise one day. i hope you have a wonderful
time!
|  Speaker: I was out walking by the lake over the weekend and there shore was
just covered in dead rotting fish.
---
Listener: Oh no! Are you ok?
Speaker: Yes, I’m okay. It was just weird to see so many dead fish
Listener: I bet that was scary.
Speaker: Yes, I would definitely not want to encounter that experience again.
Listener: That sounds like a scary experience. I’m glad you are ok.

Table 3:  Instructions and demonstration used for prompts for evaluating
empathetic listeners in freshly collected dialogs of empathetic models from
iEval with the LLM. Demonstrations and their appraisals are manually selected
from the iEval dataset. Inputs from “Positive” column were used for dialogs
conditioned on positive emotion label and inputs from “Negative” column – for
dialogs conditioned on negative emotion label.

##  Appendix B Prompt format for FED

While working with FED dataset, we used a similar template as shown in Figure
6. In a few-shot setting, we included five demonstrations instead of three,
keeping one demonstration per possible rating value. The dialogs used for
demonstrations are provided in Table 2.

The instruction used for FED dataset was we following: In such open-ended
dialogs, good listeners demonstrate coherence and maintain a good conversation
flow, they display a likeable personality and understanding of the speaker. On
the contrary, bad listeners don’t follow the context and don’t show much
interest in the conversation.