# EmoBench: Evaluating the Emotional Intelligence of Large Language Models

Sahand Sabour1 Siyang Liu2 Zheyuan Zhang3 June M. Liu4 Jinfeng Zhou1
Alvionna S. Sunaryo1 Juanzi Li3 Tatia M.C. Lee4 Rada Mihalcea2 Minlie Huang1

1 The CoAI Group, DCST, Institute for Artificial Intelligence, Tsinghua
University, Beijing, China
2 The LIT Group, Department of Computer Science and Engineering, University of
Michigan, Ann Arbor
3 The Knowledge Engineering Group (KEG), DCST, Tsinghua University, Beijing,
China
4 The Laboratory of Neuropsychology and Human Neuroscience, HKU, Hong Kong
SAR, China
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Recent advances in Large Language Models (LLMs) have highlighted the need for
robust, comprehensive, and challenging benchmarks. Yet, research on evaluating
their Emotional Intelligence (EI) is considerably limited. Existing benchmarks
have two major shortcomings: first, they mainly focus on emotion recognition,
neglecting essential EI capabilities such as emotion regulation and thought
facilitation through emotion understanding; second, they are primarily
constructed from existing datasets, which include frequent patterns, explicit
information, and annotation errors, leading to unreliable evaluation. We
propose EmoBench, a benchmark that draws upon established psychological
theories and proposes a comprehensive definition for machine EI, including
Emotional Understanding and Emotional Application. EmoBench includes a set of
400 hand-crafted questions in English and Chinese, which are meticulously
designed to require thorough reasoning and understanding. Our findings reveal
a considerable gap between the EI of existing LLMs and the average human,
highlighting a promising direction for future research. Our code and data will
be publicly available from https://github.com/Sahandfer/EmoBench.

##  1 Introduction

Figure 1: An example of the shortcomings in previous approaches for emotion
label and cause recognition and our proposed solution. In this scenario, the
perceived value of an object is directly correlated with the person’s emotion
and its intensity. Rather than extracting part of the context, this perceived
value, which serves as the cause for emotions, should be implied from the
context, increasing the difficulty and practicality of the dataset.

Emotional intelligence (EI) enables us to recognize, understand, and manage
the thoughts and feelings of ourselves and others Salovey and Mayer (1990). It
plays a pivotal role in shaping our interpersonal relationships, improving our
decision-making, and impacting our overall well-being Schutte et al. (2001,
2002); Lopes et al. (2004). Notably, emotionally intelligent systems share
similar benefits Reeves and Nass (1996), as they are perceived as more
understanding, trustworthy, and engaging Fan et al. (2017); Sidner (2016).
These traits are crucial in many areas with widespread applications such as
education, customer service, and emotional and mental health support Ivanović
et al. (2014); Del Prete (2021); Liu et al. (2021).

Recent large language models (LLMs) Bai et al. (2023); Yang et al. (2023a);
Touvron et al. (2023); OpenAI (2023) have pushed the boundaries of our
expectations regarding their potential capabilities. However, despite their
apparent proficiency in a variety of downstream tasks, such as question
answering, and summarization Zhou et al. (2023a); Zhong et al. (2023),
research on evaluating EI capabilities for LLMs has been limited. The majority
of current benchmarks Huang et al. (2023); Yang et al. (2023b); Amin et al.
(2023) assess EI through existing datasets for traditional tasks, mainly
Emotion Label and Cause Recognition. Yet, these datasets were mainly designed
as pattern recognition problems Picard (2008), encouraging models to rely on
frequent patterns and explicit information Xu et al. (2023) rather than
implications and reasoning Ghosal et al. (2022). Moreover, EI is not only
limited to recognizing emotions and their causes, but also includes the
ability to understand emotions and leverage this understanding for thought
facilitation and emotion management MacCann and Roberts (2008). We believe the
advancing capabilities of LLMs require the development of more comprehensive
and challenging benchmarks for EI. These benchmarks should go beyond
conventional tasks to fully evaluate LLMs’ understanding, reasoning, and
ability to navigate individuals’ mental states, encompassing all of the core
EI capabilities.

An example highlighting these issues is illustrated in Figure 1. Traditional
datasets typically contain samples that adhere to common patterns, such as
associating ’losing’ with ’sadness’, and include explicit information guiding
the model to extract the cause directly from the context. However, by simply
adding the perceived value of an object, the model would need to deduce the
individual’s mental state in the provided scenario to identify the
corresponding emotion and infer its corresponding cause.

Towards this end, we propose EmoBench, a theory-based comprehensive EI
benchmark for LLM evaluation, consisting of a set of 400 hand-crafted
questions, available in English and Chinese. Our framework draws upon several
established psychological theories for EI Salovey and Mayer (1990); Goleman
(1996); Schuller and Schuller (2018); O’Connor et al. (2019); Rivers et al.
(2020) and presents an extensive definition for machine EI, covering its
essential capabilities: Emotional Understanding (EU) and Emotional Application
(EA). We design emotionally sophisticated scenarios involving multiple
individuals and multi-label annotations, encompassing diverse social
situations, relationships, and emotional problems. In our evaluation, we
assess an LLM’s ability to accurately understand the emotions of the
individuals in the scenario and their causes (EU). We also evaluate whether
they can appropriately apply this understanding (EA) to facilitate their
thoughts and emotion management and identify the most effective solution
within an emotional dilemma (e.g., a family member asking for money when you
are facing financial problems yourself). Our experimental results highlight a
considerable gap between the EI capabilities of existing LLMs and humans, with
the best-performing model (GPT-4) falling short of the average human’s
performance.

To the best of our knowledge, EmoBench is the first benchmark to propose a
comprehensive framework for EI, including assessments of emotional
understanding and application. In line with our work, Wang et al. (2023) and
Paech (2023) also curated similar assessments for EI. However, their
evaluation is limited to Emotional Understanding and is also comparatively
limited in scale. We will publicly release our code and data to facilitate
future research on this topic.

Figure 2: Overview of Our Benchmark (EmoBench).

##  2 Preliminaries

###  2.1 Definition of Emotional Intelligence

The term Emotional Intelligence was coined and popularized by Salovey and
Mayer (1990) as the ability to monitor feelings of our own and understand
feelings of others, differentiate between them, and leverage this information
to guide our thoughts and actions. Since then, the rapid progress in
psychology research has expanded our understanding of EI, facilitating the
rise of new perspectives on EI Bar-On (1997); Goleman (1996); Schuller and
Schuller (2018) and improvements upon existing definitions Salovey and Mayer
(1990); Mayer et al. (1999); Rivers et al. (2020). The differences in
perspectives and definitions of EI make its assessment a non-trivial task
Waterhouse (2006), as the experimental interpretations rely heavily on the
adopted definitions and criteria. Hence, we must first identify commonalities
of existing work and establish a comprehensive definition of machine EI.

At its core, EI is a unique set of abilities. Among the most notable
definitions, Mayer et al. (1999) suggested EI is the ability to perceive,
understand, regulate, and express emotions. Goleman (1996) and Bar-On (1997)
believed competence in five aspects is indicative of high EI: knowing,
recognizing, and managing emotions in self and others, motivating oneself, and
building relationships. In addition, Schuller and Schuller (2018)’s
interpretation of EI involved emotion recognition, adapting emotions to the
situation, and leveraging emotional information to solve problems and
accomplish goals.

While there are subtle differences among these interpretations, the recurring
theme suggests that a comprehensive view of EI revolves around the ability to
accurately understand emotions, which includes perceiving, identifying, and
monitoring emotions, and appropriately applying this understanding to
accomplish a task (e.g., managing emotions and facilitating our thoughts and
decisions). Hence, we designed our evaluation framework to encompass these two
salient dimensions: Emotional Understanding (EU) and Emotional Application
(EA).

###  2.2 Measures of Emotional Intelligence

In psychology, EI evaluation is mainly classified into trait and ability
measures Ashkanasy and Daus (2005). Trait measures are commonly assessed
through self-report questionnaires and designed to explore how individuals
respond to scenarios that evoke emotionsO’Connor et al. (2019). However, self-
report assessments are not suitable for evaluating LLMs. On the other hand,
ability measures target individuals’ emotional understanding and performance
and provide a more theoretical view of EI, and they are more commonly employed
for assessing EI Conte (2005). Among them, the Mayer–Salovey–Caruso Emotional
Intelligence Test (MSCEIT) Mayer et al. (2007) and MacCann and Roberts
(2008)’s situational tests for emotion understanding and management (STEU and
STEM), have become the most frequently adopted tools in the literature
O’Connor et al. (2019). These measures include sets of meticulously designed
multiple-choice questions, with each set targeting a specific EI ability.

##  3 EmoBench

We believe EI benchmarks should be comprehensive and aim to transcend general
patterns while necessitating a deep level of reasoning and understanding.
Therefore, based on our established definition for machine EI (§2.1) and
existing tools for EI assessment in psychology (§2.2), our framework includes
a multi-faceted evaluation of LLMs’ emotional understanding and reasoning,
while also exploring LLMs’ emotional awareness and mentalizing capabilities by
analyzing their response to emotional dilemmas and their application of
emotional understanding.

Figure 2 presents an overview of EmoBench. First, through synthesizing several
established psychological theories for EI Salovey and Mayer (1990); Goleman
(1996); Rivers et al. (2020), we identified and taxonomized essential
capabilities for the established dimensions: Emotional Understanding (EU) and
Emotional Application (EA). Accordingly, based on these taxonomies, we crafted
a series of emotionally sophisticated situations involving one to three
individuals.

Creating challenging scenarios that involve implications and do not rely on
common patterns requires substantial creativity and diversity, which makes
manual data collection a non-trivial task. Therefore, using the designed
category descriptions, we initially prompted GPT-4 OpenAI (2023) to generate
example scenarios. However, while GPT-4 produced the best results in our
preliminary experiments among the adopted LLMs, the generated scenarios
included explicit mentions of emotion labels and their causes and required
minimum reasoning and understanding to reach the correct answer, lacking
emotional depth and coverage. Therefore, we used the generated examples as
inspiration to increase our topic diversity and manually crafted the scenarios
in our dataset. Lastly, we annotated each scenario based on each dimension’s
design and requirements, which we will discuss in the following sections. For
the remainder of this section, the authors who collected and annotated the
data will be referred to as workers.

###  3.1 Emotional Understanding

Emotion Recognition has become a popular research direction in NLP over the
past two decades as it is an essential skill for emotionally intelligent
machines Picard et al. (2001). There exist several datasets that are commonly
used for this task, such as MELD Poria et al. (2019), DailyDialog Li et al.
(2017), and GoEmotions Demszky et al. (2020). These datasets mainly provide an
emotion-stimulating scenario and a corresponding emotion label for the person
involved in the situation (e.g., I broke up with my girlfriend $\rightarrow$
Sad). Following this trend, an auxiliary task, namely Emotion Cause
Recognition Poria et al. (2021), was proposed to assess whether language
models can learn to identify the causes of emotions in addition to their
labels in given scenarios (e.g., I’m getting married soon $\rightarrow$
getting married $\rightarrow$ Excited).

There are two fundamental problems with the design of these traditional
datasets. First, previous work considers emotion recognition as a pattern
recognition problem Picard (2008); Schuller and Schuller (2018), in which
models predict the most likely emotion label for the situation based on the
observed patterns in the training set. With this approach, there is no
reasoning or understanding involved nor required to reach the desired output,
a trait we believe is necessary for evaluating modern LLMs due to their
emerging capabilities. Moreover, current datasets for cause recognition are
designed as span extraction problems, requiring the cause to be explicitly
stated and removing the need for understanding the individual’s mental state
and reasoning about implications.

However, we believe combining these two tasks lays a solid foundation for
assessing emotional understanding. Hence, while keeping the same format, we
create more challenging scenarios in which merely relying on common patterns
would not lead to the correct response, and understanding emotional
implications and thorough reasoning is necessitated. Moreover, as many of our
designed scenarios involve multiple individuals, our assessment targets
understanding the various perspectives of the same situation, which leads to
differences in the experienced emotions.

#### Data Collection and Annotation

Our designed taxonomy for this dimension predominately assesses LLMs’
comprehension of four essential categories that are indicative of emotional
understanding: complex emotions, emotional cues, personal beliefs and
experiences, and individual perspectives (perspective-taking). Each category
consists of several sub-categories, targeting its various aspects. More
descriptions and examples are provided in Appendix A.

Subsequently, in our framework, we need to annotate the labels and causes for
the emotions of the people involved in the scenario. We adopt Plutchik’s wheel
of emotions Plutchik (1982) as the foundation of our emotion classification
taxonomy, due to its comprehensive and scalable design. In addition, we
aggregate the emotion labels from previous work Ekman (1984); Li et al.
(2017); Rashkin et al. (2018); Demszky et al. (2020) to augment our emotion
categories, creating a unified categorization. More details on the emotion
taxonomy are provided in Appendix B.

Figure 3: Category Distribution in EmoBench. Main categories are depicted on
the chart and secondary categories are provided below the chart.

Following the design of our taxonomies, each worker manually created
emotionally challenging scenarios and annotated the emotion label and cause
for the people involved. They also created additional labels for emotions and
causes to form multiple-choice questions (MCQs). In our framework, a scenario
involving three people would result in three separate MCQs for each
individual’s emotions and their causes, respectively. Subsequently, one worker
was assigned to translate the MCQ (into English if the original was written in
Chinese, and vice versa, based on the worker’s language fluency) and, with the
addition of two other workers, meticulously review its content to ensure data
quality and overall agreement. In total, we created 121 scenarios involving
1-3 individuals, leading to 200 MCQs challenging MCQs. Figure 3 shows the
corresponding category distributions (emotion distributions provided in
Appendix C).

###  3.2 Emotional Application

Despite emotional understanding being a critical part of EI, it is also
essential to analyze how LLMs use this knowledge to facilitate thoughts and
manage emotions when faced with emotionally sophisticated problems Goleman
(1996). Inspired by MacCann and Roberts (2008), we propose a novel task for
assessing LLM’s EI: Emotional Application. In this task, we aim to evaluate
LLMs’ proficiency in leveraging their emotional understanding of the
individuals’ mental states in a given scenario and identifying the most
effective course of action or response within an emotional dilemma.

We create our scenario based on different Relationships and Problems. Similar
to Zhou et al. (2023b), we only consider two types of relationships in this
work: personal (e.g., friends, family, romantic partners) and social (e.g.,
boss, teacher, coworkers), and leave more detailed categorizations to future
work. Accordingly, a situation involving these relationships could contain
problems that we (self) or others are facing. Issues arising from
interpersonal conflicts or arguments are also considered problems with others.
Lastly, we would prompt the LLM to find the most effective solution to the
presented dilemma, which is either an action (i.e., what to do?) or a response
(i.e., what to say?).

#### Data Collection and Annotation

Similar to Section 3.1, each worker was tasked with designing scenarios based
on the generated examples and the assigned categories, and creating multiple
plausible solutions to the presented dilemma. Workers were encouraged to
increase the MCQ’s difficulty by introducing implications in the scenario and
adding plausibility to all of the choices. Subsequently, a second worker
revised and translated the scenario and choices (English $\rightarrow$
Chinese, and vice versa).

Given that this could be seen as a subjective task, we assigned the original
two workers alongside two new workers to annotate each MCQ and determine its
label. Inspired by MacCann and Roberts (2008), workers were asked to
distribute four units of $0.25$ based on their preference as scores for the
available choices ($\sum\text{Scores}=1$). For instance, for choices
$\\{a,b,c,d\\}$, if a worker believes choices $a$ and $b$ are both plausible
but prefers $a$ over $b$, the annotated score would be $\\{0.75,0.25,0,0\\}$.
Then, we averaged the scores from all annotators to define the most effective
answer for each dilemma. The inter-annotator agreement using Fleiss’ Kappa
Fleiss and Cohen (1973) was $\kappa=0.852$), indicating excellent agreement
and an objectively correct answer for the majority of the collected questions.
Overall, we curated a set of 200 MCQs, with each relationship-problem-solution
triplet (e.g., social-self-action) containing 25 items.

##  4 Experiments

###  4.1 Task Formulation

All of our designed tasks take the form of multiple-choice questions (MCQ).
For each MCQ in the Emotional Understanding task, we first ask the LLM to
identify the individual’s emotion and, subsequently, choose the corresponding
cause. In the Emotional Application task, we simply ask the LLM to choose the
most effective response or action in the given scenario. We evaluate LLMs in
two settings: zero-shot prompting with task instruction and the MCQ with and
without chain-of-thought reasoning Wei et al. (2022), namely Base and +CoT,
respectively. Our designed prompts are provided in Appendix D.

During our evaluation, we prompt the model to choose the correct answer five
times (5-shot) for each question and use majority voting (i.e., the most
frequently chosen answer) to determine the model’s choice. Subsequently, we
implemented a series of heuristic rules to parse the generated outputs. Since
LLMs have shown to have a bias towards choice ordering Zheng et al. (2023), we
randomly modify the choice ordering three times and repeat the above process
for each new permutation. Lastly, we calculate and report the average accuracy
of the four runs.

Task | Emotional Understanding (EU) | Emotional Application (EA)
---|---|---
Model | Base | +CoT | Base | +CoT
EN | ZH | EN | ZH | EN | ZH | EN | ZH
Yi-Chat (6B) | 12.75 | 18.62 | 11.62 | 13.75 | 47.25 | 51.62 | 44.00 | 40.62
ChatGLM3 (6B) | 20.25 | 20.62 | 20.38 | 21.12 | 55.62 | 46.75 | 52.88 | 53.75
Llama2-Chat (7B) | 11.75 | 8.25 | 6.38 | 6.50 | 50.12 | 39.25 | 31.88 | 27.25
Baichuan2-Chat (7B) | 22.38 | 19.12 | 18.88 | 17.25 | 52.50 | 44.50 | 48.62 | 45.25
Qwen-Chat (7B) | 22.50 | 20.62 | 21.38 | 16.25 | 54.62 | 46.62 | 44.12 | 52.50
Llama2-Chat (13B) | 18.12 | 13.12 | 12.62 | 9.88 | 55.88 | 50.62 | 37.75 | 33.62
Baichuan2-Chat (13B) | 26.25 | 26.62 | 19.38 | 22.00 | 53.62 | 54.75 | 51.00 | 45.12
Qwen-Chat (14B) | 35.50 | 31.50 | 30.12 | 30.25 | 60.50 | 58.12 | 43.25 | 58.00
Baichuan2-Chat (53B) | 34.88 | 34.00 | 33.00 | 33.00 | 65.38 | 62.00 | 65.12 | 63.38
ChatGLM3 (66B) | 36.12 | 35.38 | 33.75 | 29.50 | 65.50 | 59.12 | 63.12 | 60.12
GPT 3.5 | 33.12 | 26.38 | 33.88 | 26.62 | 61.38 | 55.75 | 62.38 | 57.38
GPT 4 | 59.75 | 54.12 | 58.25 | 51.75 | 75.50 | 73.75 | 75.88 | 73.50
Random | 2.62 | 24.12
Table 1: Results of evaluation on EmoBench (accuracy %). The best results are
highlighted in Bold.

###  4.2 Models

In our experiments, we adopt a range of recent widely-used LLMs that have
shown promising performance on existing benchmarks Zhang et al. (2023). For
closed-source models accessible through
APIs111https://api.openai.com/v1/chat/completions, we evaluate OpenAI’s GPT 4
(gpt-4) and GPT 3.5 (gpt-3.5-turbo) OpenAI (2023), the 66B version of ChatGLM
3 Du et al. (2022); Zeng et al. (2022), and the 53B version of Baichuan 2 Yang
et al. (2023a). For open-source models, we included the smaller open-source
versions of ChatGLM 3 (6B) and Baichuan 2 (7B and 13B). In addition, we
experimented with two sizes of Llama 2 (7B and 13B; Touvron et al. (2023)) and
Qwen (7B and 14B; Bai et al. (2023)), and included
Yi222https://github.com/01-ai/Yi, a recently open-sourced 6B Llama-based
model. Following Ismayilzada et al. (2023), we also included Random chance
(i.e., randomly choosing one of the choices) as a baseline.

###  4.3 Implementation Details

For Llama-based models, we used the default generation hyperparameters (top-p
sampling with $p=0.9$ and temperature $=0.6$). For others, we directly
employed their pre-defined interfaces, either through their online API or the
chat function in the Transformers
library333https://github.com/huggingface/transformers. All of our experiments
were run on single A100 80GB GPUs.

##  5 Results and Findings

Our obtained results are provided in Table 1. Overall, GPT-4 significantly
outperformed the other models in both tasks. In general, apart from GPT-4, all
models demonstrated better accuracy than random chance and had slightly
varying performances in the studied dimensions. Larger models performed
considerably better than their smaller baselines, which is consistent with
previous findings on parameter scaling Brown et al. (2020). Notably, open The
task’s language did not have a significant impact on the performance, with all
models (excluding Yi) generally performing slightly better in English, which
we believe could be due to data distributions in the original training data.
This could also explain why Chinese-based LLMs (e.g., Yi) outperform their
English-based (e.g., LLama2-7B) counterparts in the Chinese subset of EmoBench
despite having a similar number of parameters. However, as we do not have
access to the LLMs’ pertaining data, we cannot claim any correlations between
the training data and performance on our benchmark.

Figure 4: Results on the EmoBench subset used in the human evaluation.

All LLMs found emotional understanding comparatively more challenging than its
application. We believe this is due to several reasons. Contrary to the EA
task, the EU samples require models to correctly answer two questions
(identifying 1. emotions and 2. causes), which itself serves as a bigger
challenge. Moreover, evidenced by differences in their designs, the EU
questions aimed to portray situations that included various implications and
outcomes for frequent patterns. However, our design of EA samples was still
prone to including such patterns as with this task, our main goal was to
present a novel evaluation of LLMs’ awareness and management when faced with
emotional dilemmas. Hence, the difficulty of the EA task would naturally be
much lower.

A notable finding in our observations was that requiring LLMs to reason step-
by-step generally had little to no improvements, even hindering the
performance for smaller models(<14B). Upon further investigation, we found
this occurred due to several factors. For smaller models, hallucinations and
misassumptions (e.g., believing that a person who initially considered leaving
to have left already), wrong perspective-taking (e.g., considering the
emotions of the other individuals involved in the scenario instead), mistakes
in emotional understanding (e.g., receiving old photos as a gift leads to
embarassment).

In addition, in several cases, LLMs’ reasoning led to a change of topic (e.g.,
turning to a detailed discussion on the necessity of being empathetic in
modern society when faced with a scenario about supporting a loved one within
an emotional dilemma) or refusal to answer (stating that none of the options
are correct). Such errors were considerably less common in larger models
(>50B), which is indicated by the smaller gaps between their performance with
and without CoT. However, these results are expected as more reliable
reasoning capabilities emerge when the parameters are scaled above certain
thresholds Wei et al. (2022).

Notably, we believe appropriate reasoning for our tasks would involve
traversing the events within the provided scenario and following the
transitions in the individual’s emotions, demonstrating an understanding of
their mental state and the situation’s implications. However, our experiments
showed that LLMs’ reasoning mainly involved analyzing the provided choices and
evaluating the validity of each choice. While this could be an effective
strategy for filtering out the wrong responses, this form of reasoning may
overlook the nuanced emotional awareness and considerations involved in human
decision-making, which are pivotal parts of EI.

##  6 Comparison with Human Performance

To obtain a baseline for human EI, we recruited annotators through online
surveys to participate in our EI test. More information on our recruitment
process, quality control, and participant demographics are provided in
Appendix E. In total, we recruited 48 participants and allocated an equal
number of participants to each language-task evaluation pair. Subsequently,
for each group, we randomly sampled 30 MCQs from EmoBench that were not
included in the initial screening process.

As shown in Figure 4, our human participants outperformed the LLMs on both
tasks. Notably, although GPT-4, the top-performing model, came close to the
average human performance, particularly in the EA task, it still fell short of
surpassing individuals with higher emotional intelligence, highlighting a
significant gap in current LLMs.

Emotional Understanding (EU)
---
Scenario | Despite countless rejections, Susan kept pursuing her goal of finding a job. Today, she
|  finally received an offer and started crying. At this moment, her mom
walked into the
| room and ran to embrace her after seeing her cry.
Question | Susan’s mom feels …? (a) Relief (b) Nervousness (c) Anger (d) Delight
LLM |  (d) Delight $\rightarrow$ Her daughter was finally able to get a job offer.
Scenario | I have acrophobia. Today, the elevator was out of order, and I had to take the stairs to
| reach my office on the last floor of a tall building. I considered it a nice
little exercise.
Question | I feel …? (a) Excitement (b) Fear (c) Disapproval (d) Nervousness
LLM |  (b) Fear $\rightarrow$ I was afraid of taking the stairs due to my acrophobia.
Scenario | I have a teacher who gives the F grade as the  highest mark since he believes F stands
| for Full. I just got the results from yesterday’s hard test and saw that he
gave me an F.
Question | In this situation, I feel …? (a) Disappointment (b) Nervousness (c) Pride  (d) Sadness
LLM |  (a) Disappointment $\rightarrow$ I failed the difficult test
Emotional Application (EA)
Scenario | Peter’s best friend jokingly tells him that he is the reason why their group of friends
|  keep losing at video games.
Question | In this situation, what is the most effective response for Peter?
| (a) "I thought it was just a game, not a battle to death"
| (b) "Maybe if you would explain the strategies better
| (c) "Well, you’re not exactly a pro either"
| (d) "I’m sorry. I’ll try to improve my skills so we can win"
LLM |  (d) $\rightarrow$ it shows accountability and a willingness to take action to improve the situation.
Table 2: Examples of common mistakes made by LLMs in EmoBench. LLM represents
the studied models’ general response. The content is summarized due to space
limitations.  Green indicates the correct answer.

##  7 Error Analysis

To provide a qualitative view of LLMs’ performance on our benchmark, we
present several examples of common mistakes in the studied tasks in Table 2.
For EU questions, LLMs tend to make mistakes mainly due to misassumptions
(e.g., a person walking in the door would not immediately know what is going
on), reliance on frequent patterns (e.g., having a phobia would not
necessarily lead to fear), and inability to reason (e.g., getting an F is not
a failure when its the highest score).

With EA questions, LLMs’ answers mainly exhibited a preference for more
general solutions, disregarding the relationship between individuals, which
could highly impact their evoked emotions and subsequent responses. For
instance, while the best course of action when facing criticism may be taking
accountability and focusing on self-improvement, gentle humor would be a more
suitable response to a friend’s simple tease as it shows better emotional
regulation and more awareness.

##  8 Conclusion and Future Work

In this paper, we introduced EmoBench, a theory-based, comprehensive, and
challenging set of 400 hand-crafted MCQs, including emotionally sophisticated
scenarios, for assessing Emotional Intelligence (EI) in Large Language Models
(LLMs) through EI’s two salient dimensions: Emotional Understanding and
Emotional Application. Our results revealed that while larger LLMs
significantly outperform smaller models, there is still a considerable gap
between the best-performing LLM in our study and the average human’s EI.

In the future, we hope that by facilitating EI evaluation, EmoBench can
encourage research on emotionally intelligent LLMs, leading to models that are
more capable of understanding human emotions and applying this understanding
in many promising tasks, such as emotional and mental health support Sabour et
al. (2022). In addition, we plan to augment EmoBench with more data, exploring
the more fine-grained features (e.g., personal characteristics and language
expression).

##  9 Limitations

In this work, we aimed to ensure high annotation quality and difficulty with
our curated samples, which required intensive labor and manual supervision,
and thus, compared to existing benchmarks for other tasks, EmoBench is limited
in scale. Given our team’s background, we were only able to collect data in
English and Chinese, which are two of the most prevalent languages worldwide.
We believe translating our benchmark to other languages, particularly low-
resourced languages, could reveal more insights into their seemingly
emotionally intelligent behavior.

In addition, our benchmark is limited to a single modality (text) as most of
the recent prevalent LLMs are text-based. However, many psychological tests
for emotional intelligence (e.g., MSCEIT; Mayer et al. (2007)), include
assessments of various modalities, such as the individual’s tone and facial
features. Moreover, while we did not directly include samples from GPT-4, we
leveraged its generated examples to inspire our MCQs, which might have
introduced a bias in our benchmark. With future improvements in LLMs, we will
continue exploring different dimensions of EI and augment our benchmark
accordingly.

Within our evaluation, we acknowledge the choice of prompts could have had a
significant influence on the LLMs’ performance. However, despite our emphasis
on prompt-tuning and the many iterations of prompt designs for our tasks, we
cannot claim our prompts were optimal, and thus, the experimental results are
not indicative of LLMs’ peak performance in EI. Moreover, we only experimented
with chain-of-thought reasoning to augment the output, which future work could
expand upon and propose new reasoning techniques that better apply to
emotional scenarios.

Emotional intelligence is still an abstract concept in the field of psychology
and our view on it may change with developing research. Similarly, emotions
are not objective, and individual responses to the same situation could vary
significantly. We strived to design our scenarios and the corresponding
choices in a manner that would only require a general and commonsensical
understanding of emotions. The trade-off here, particularly for designing
scenarios for emotional application, was that we only included scenarios that
all the annotators had experienced to ensure reliable annotation, limiting the
scope of the topics and relationships covered. In addition, we did not study
the effect of more fine-grained personal traits (e.g., detailed experiences,
characteristics, and language expression) on the experienced emotions, as we
found it outside of our scope. For instance, during a conflict or
confrontation, a person who deals with issues by making jokes may not
experience the same level of anger as a serious individual. We believe future
work could explore augmenting our benchmark with more cases and study the
effects of these more fine-grained traits.

## Ethical Considerations

We emphasize that our evaluation is concerned with the perceived view of
emotional intelligence, aiming to explore the limitations of existing LLMs
through novel and challenging tasks. In this work, while our proposed
definition includes the ability to understand emotions and apply this
understanding to manage emotions, we do not claim nor believe that large
language models are capable of possessing or simulating emotions. With our
experiments, we demonstrated that LLMs still rely on frequent patterns to
indicate signs of understanding. In addition, despite not having emotions, we
found that LLMs can capitalize on their seen patterns to show apparent signs
of emotional sense and awareness, which is in line with previous research on
LLMs’ commonsense Sap et al. (2019) and morality Jiang et al. (2021).

## Acknowledgements

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##  Appendix A Scenario Taxonomy

Our scenario taxonomy is as follows:

###  A.1 Complex Emotions

Understanding complex emotions is an essential part of emotion understanding
Rivers et al. (2020). In our framework, we include three categories that cover
the essential aspects of complex emotions:

  * •

Emotion Transition: In response to different events, our emotions are subject
to change. To assess whether LLMs can reason about such transitions in one’s
emotions, we create scenarios in which the individual’s emotion changes based
on the turn of events.

> A mother who is annoyed about ruining the food, would be delighted when
> their child enjoys and compliments it.

  * •

Mixture of Emotions: while previous work mainly annotates each sample with a
single emotion label Li et al. (2017); Rashkin et al. (2018), many individuals
tend to experience a combination of emotions in various situations. Such
emotions could be of the same (e.g., happy and excited) or the opposite (e.g.,
sad yet relieved) polarities. Hence, we designed scenarios in which the
individual feels a mixture of emotions.

> If two friends, Annie and Mark, participate in the same competition and
> Annie gets first place, then Mark would be happy and proud for his friend’s
> accomplishment while being disappointed for his loss.

  * •

Unexpected Outcome: Inspired by Dyck et al. (2001), we create scenarios in
which the conclusion contradicts explicit commonsense and expected reactions.
We believe this is crucial in assessing whether LLMs are reliant on patterns
to understand emotions, as these scenarios involve reactions that are uncommon
for displaying the emotion in the corresponding scenarios.

> If Jamie has had a bad day full of misfortune and bad luck, and finally
> starts laughing hysterically after dropping his ice cream, his laughter
> shows frustration, not amusement.

###  A.2 Personal Beliefs and Experiences

To have a deep understanding of one’s emotions, we need to recognize how their
beliefs and values among past experiences and appraisals could impact the
emotions they experience Rivers et al. (2020). To assess this, we designed
three categories that aim to evaluate LLM’s comprehension of how individual’s
Cultural Values, Sentimental Values, and personal experiences and traits
(namely Persona) could affect their reaction to certain events.

  * •

Cultural Values: In these scenarios, we aim to assess whether LLMs are capable
of understanding how an individual’s reaction to the same event could vary
based on their cultural values and background Rivers et al. (2020). Consider
the following situation. Anna is brought up in a culture where being late is
considered rude. However, Jonah’s culture does not put a great emphasis on
punctuality.

> If Anna is late to a meeting with Jonah, she would be embarrassed and
> apologetic, while Jonah would be unbothered.

  * •

Sentimental Value: Similarly, an important aspect of understanding a person’s
emotion is identifying the sentimental value that they assign to different
memories and belongings.

> Losing a T-shirt we wanted to throw out (low sentimental value) is unlikely
> to lead to sadness, whereas it would be devastating if the T-shirt was a
> gift from a lost family member (high sentimental value).

  * •

Persona: we also wanted to analyze whether LLMs comprehend the reactions of
people with pre-existing emotions. These could include phobias, appraisals
(previous experiences), and personal traits (e.g., being antisocial or
extroverted).

> If a person with claustrophobia, who gets extremely uncomfortable in small
> or crowded spaces, is invited to a small space, they might experience fear,
> but not when going to a spacious garden space.

>

###  A.3 Emotional Cues

Emotional intelligence enables us to recognize and understand cues about
emotions of ourselves and others Rivers et al. (2020). While recent research
has shown that LLMs are capable of understanding and responding to direct and
explicit emotional stimuli and cues Li et al. (2023), it is not explored how
such models would react to implicit cues. To this end, we designed this
category to assess LLM’s comprehension of text-based vocal (e.g., vocal
utterances, tone, and speech) and visual (e.g., facial/physical expressions)
cues of emotions.

> A person’s face turning red could be a visual cue for being angry or shy. A
> sigh could indicate relief or annoyance.

###  A.4 Perspective Taking

Emotional understanding has significant correlations with affective theory-of-
mind Mier et al. (2010); Kalbe et al. (2010); Ferguson and Austin (2010),
mainly in that they both require the ability to view situations from the
perspective of others and simulate their emotions given the circumstances,
formally known as perspective-taking. Therefore, we adopt three of the
prevalent tasks for assessing perspective-taking in theory-of-mind: False
Belief, Faux Pas, Strange Story. However, contrary to the traditional
implementation of these tests, our sole focus is on designing scenarios that
trigger different emotions based on personal knowledge and views of the
situation.

  * •

Affective False Belief: The Sally-Ann test Baron-Cohen et al. (1985) is one of
the de facto assessments for the theory of mind (ToM), i.e., the ability to
infer the beliefs and mental states of others. Recently, it has also been
widely adopted for evaluating ToM in LLMs He et al. (2023); Ma et al. (2023);
Kim et al. (2023) as it requires reasoning about each individual’s knowledge
and perspective on the situation to answer the corresponding questions. In our
framework, we collected scenarios in which the individual’s emotions could be
implied through reasoning about their beliefs, which could be affected by
trusting the word of others and/or being oblivious to certain events.

> I was the only one who saw my friend’s grades and realized that he failed
> the exam. Therefore, if I tell him that he passed the exam with flying
> colors, he would be excited, not disappointed.

  * •

Faux Pas: Similarly, a more advanced assessment of ToM is conducted through
the faux pas (i.e., tactless acts or remarks that cause unintentional negative
consequences) detection test Baron-Cohen et al. (1999). In this task,
participants are presented with a social situation and are required to detect
the presence and identify the faux pas. Inspired by this, we include a series
of scenarios that include a faux pas and assess LLMs on identifying the
emotions of the involved individuals. In these scenarios, in addition to
understanding social cues associated with a faux pas, LLMs also have to reason
about each individual’s beliefs and their known information to understand
their emotions.

> If a person openly criticizes a painting without knowing it was drawn by
> their brother, then they may feel disgust towards the painting and not
> embarrassment due to their lack of information.

  * •

Strange Story: Inspired by Happé (1994), we also designed scenarios that
establish hypothetical grounds and imaginary assumptions that would contradict
the normal pattern of behavior. This further evaluates whether LLMs truly
reason about the situation to infer the relevant emotions or base their
judgments on learned patterns.

> While getting an F in a test would regularly lead to disappointment, getting
> an F in a class where the teacher only gives Fs to the highest mark leads to
> pride.

##  Appendix B Emotion Taxonomy

Figure 5 demonstrates our designed emotion taxonomy. At its core, Plutchik’s
design involves eight basic emotions with varying intensities, and other
emotions are created and labeled as a mixture of these basic emotions. For
instance, the basic emotion Disgust could turn into Boredom or Loathing with
low and high intensities, respectively. It could also mix with Sadness to
create the feeling of Remorse. This design facilitates the addition of new
labels by mixing different emotions and seamless scaling of our taxonomy.

Figure 5: Our Emotion Classification Taxonomy.

##  Appendix C Emotion Distribution

Figure 3 demonstrates the category distribution for the collected samples.

Figure 6: Emotion Distribution in EmoBench.

##  Appendix D Experiment Prompts

Our designed prompts are demonstrated in Table 3. For Chinese samples, we
directly translated the provided prompts into Chinese.

System Prompt (Base)
---
**Instructions**
In this task, you are presented with a scenario, a question, and multiple
choices. Please carefully analyze the scenario and take the perspective of the
individual involved.
**Note**
Provide only one single correct answer to the question and respond only with
the corresponding letter.
Do not provide explanations for your response.
System Prompt (CoT)
**Instructions**
1\. **Reason**: Read the scenario carefully, paying close attention to the
emotions, intentions, and perspectives of the individuals involved. Then,
using reason step by step by exploring each option’s potential impact on the
individual(s) in question. Consider their emotions, previous experiences
mentioned in the scenario, and the possible outcomes of each choice.
2\. **Conclude** by selecting the option that best reflects the individual’s
perspective or emotional response. Your final response should be the letter of
the option you predict they would choose, based on your reasoning.
**Note**
The last line of your reply should only contain the letter numbering of your
final choice.
Emotional Understanding (EU)
For Emotions
Scenario: $[$scenario$]$
Question: What emotion(s) would $[$subject$]$ ultimately feel in this
situation?
Choices: $[$choices$]$
For Causes
Scenario: $[$scenario$]$
Question: Why would $[$subject$]$ feel $[$emotions$]$ in this situation?
Choices: $[$choices$]$
Emotional Application (EA)
Scenario: $[$scenario$]$
Question: In this scenario, what is the most effective $[$problem type$]$ for
$[$subject$]$?
Choices: $[$choices$]$
Answer
Without CoT $\rightarrow$ Answer (Only reply with the corresponding letter
numbering):
With CoT$\rightarrow$Answer: Let’s think step by step
Table 3: Our designed Prompts

##  Appendix E Human Evaluation

| EU | EA
---|---|---
| ($\mathbf{n=24}$) | ($\mathbf{n=24}$)
Gender, $\mathbf{n}$ (%) | M | 13 (54.17%) | 8 (33.3%)
| F | 11 (45.83%) | 16 (66.67%)
Age, Mean (SD) |  | 23.42 (3.62) | 23.3 (1.98)

Table 4:  Demographics of Our Human Participants ($n=48$). M and F indicate
Male and Female, respectively.

During the registration for our experiments, all candidates disclosed their
demographics as well as their language and task preferences for the
experiment. As a part of our annotation quality control, we excluded
individuals under the age of 21 as a means of ensuring emotional maturity (the
ability to understand and manage emotions; Jobson (2020)). In addition, we
required each candidate to correctly answer all of the questions (six MCQs) in
a randomly sampled subset of our benchmark.

A total of 70 individuals registered for the experiment. From this candidate
pool, we recruited a total of 48 participants (31.43% rejection rate) based on
the above criteria and their pre-disclosed language-task preferences. Our
participants’ demographics are summarized in Table 4. All the candidates were
informed of the purpose of our study and consented to participate in our
experiments. Accordingly, we allocated an equal number of candidates to each
language-task evaluation pair ($n=12$). For each group, we randomly sampled 30
MCQs that were not included in the initial screening process. Each participant
was compensated 14.28$ per hour, which is well over the minimum wage in the
US444https://www.dol.gov/general/topic/wages/minimumwage. In addition, we also
provide the participant guidelines in Figure 7 .

Figure 7: Participant guidelines for our human evaluation experiments.