E mo B ench: Evaluating the Emotional Intelligence of Large Language Models (2024)

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 management 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 are publicly available at https://github.com/Sahandfer/EmoBench.

## introduction emotional intelligence (ei) enables us to recognize, understand, and manage the thoughts and feelings of ourselves and others @xcite . it plays a pivotal role in shaping our interpersonal relationships, improving our decision-making, and impacting our overall well-being @xcite @xcite . notably, emotionally intelligent systems share similar benefits @xcite , as they are perceived as more understanding, trustworthy, and engaging 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. @xcite . these traits are crucial in many areas with widespread applications such as education, customer service, and emotional and mental health support @xcite @xcite . recent large language models (llms) @xcite @xcite 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 @xcite , research on evaluating ei capabilities for llms has been limited. the majority of current benchmarks @xcite @xcite assess ei through existing datasets for traditional tasks, mainly emotion label and cause recognition. yet, these datasets were mainly designed as pattern recognition problems @xcite , encouraging models to rely on frequent patterns and explicit information @xcite rather than implications and reasoning @xcite . 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 @xcite . 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 provided 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 an object's perceived value, 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 handcrafted questions, available in english and chinese. our framework draws upon several established psychological theories for ei @xcite @xcite @xcite 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 eval-uation, 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 llm (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, @xcite 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. ## emobench we believe ei benchmarks should be comprehensive and transcend general patterns while necessitating deep 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 multifaceted evaluation of llms' emotional understanding, 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 @xcite @xcite , we identified and taxonomized essential capabilities for the established dimensions: emotional understanding (eu) and emotional application (ea). accord-ingly, 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 @xcite 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. ## results and findings our obtained results are provided in tables 1 and 2 . overall, gpt-4 significantly outperformed the other llms in both tasks. in general, all llms demonstrated better accuracy than random chance. however, in the eu task, several of the smaller models had worse performance than simply choosing the most frequent choice. an interesting finding in our experiments was that requiring llms to reason step-by-step generally had little to no improvements, even hindering the performance for smaller models (@xmath0. we will further investigate this issue in section 7. notably, the task's language did not have a significant impact on the performance, with all llms (excluding yi and chatglm-6b) performing slightly better in english, which we believe could be due to data distributions in their training data. this could also explain why chinese-based llms (e.g., yi) outperform their english-based counterparts, such as llama 2 (7b), in the chinese subset of emobench despite having a similar size. however, as we do not have access to the llms' pertaining data, we cannot claim any correlations between their training data and performance on our benchmark. moreover, the performance consistently improved with increased parameters, which is consistent with previous findings on llm scaling @xcite . all llms found emotional understanding considerably more challenging than its application. we believe this is due to several reasons. contrary to the ea task, the eu samples require llms to correctly answer two questions (the emotion and its cause), which itself serves as a bigger challenge. this is also indicated by the results from the random and majority baselines. 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. ## comparison with human performance to obtain a baseline for human ei, we recruited participants through online surveys to complete our ei test. more information on our recruitment process, quality control, and participant demographics are provided in appendix d. 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 5 , our human participants outperformed the llms on both tasks. notably, although gpt-4, the top-performing llm, 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. ## error analysis to provide a qualitative view of llms' performance on our benchmark, we analyzed llms' generated reasoning through cot. 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 analysis 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. we observed that llms' step-by-step reasoning occasionally led to changes in the 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 (tables 1 and 2 ). however, these results are expected as more reliable reasoning capabilities emerge when the parameters are scaled above certain thresholds @xcite . moreover, we present several examples of common mistakes made by llms in 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) @xmath2. 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) @xmath3. 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) → it shows accountability and a willingness to take action to improve the situation. by having misassumptions (e.g., a person walking in the door would not immediately know what is going on), and incorrect reasoning (e.g., having a phobia would not necessarily lead to fear or getting an f is not a failure when its the highest score). we believe these errors mainly occur due to llms' lack of emotional understanding, such as weak perspective taking (as shown in ## conclusion and future work in this paper, we introduced emobench, a theorybased, comprehensive, and challenging set of 400 hand-crafted mcqs, including emotionally sophisticated scenarios, for assessing emotional intelligence (ei) in large language models through its two salient dimensions: emotional understanding and emotional application. our results revealed that existing llms struggle with emotional intelligence (mainly understanding), and there is still a considerable gap between the best-performing llm in our study and the average human. we hope that by facilitating ei evaluation, emobench can encourage research on emotionally intelligent llms, leading to llms that are more capable of understanding emotions and applying this understanding in many promising tasks, such as emotional and mental health support @xcite . in addition, we plan to augment emobench with more data, exploring the more fine-grained features.