EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models (2024)

Abstract: Emotional intelligence in large language models (LLMs) is of great importance in Natural Language Processing. However, the previous research mainly focus on basic sentiment analysis tasks, such as emotion recognition, which is not enough to evaluate LLMs’ overall emotional intelligence. Therefore, this paper presents a novel framework named EmotionQueen for evaluating the emotional intelligence of LLMs. The framework includes four distinctive tasks: Key Event Recognition, Mixed Event Recognition, Implicit Emotional Recognition, and Intention Recognition. LLMs are requested to recognize important event or implicit emotions and generate empathetic response. We also design two metrics to evaluate LLMs’ capabilities in recognition and response for emotion-related statements. Experiments yield significant conclusions about LLMs’ capabilities and limitations in emotion intelligence.

text: Findings of the Association for Computational Linguistics ACL 2024, pages 2149–2176 August 11-16, 2024 ©2024 Association for Computational Linguistics
1 Introduction Emotional intelligence in humans has long been a topic of interest in psychological research. Emotionally intelligent individuals possess the capability to perceive, use, understand, and manage emotions effectively (Colman, 2015). The concept of emotional intelligence has been widely recognized, and many researchers argue that it can be considered as a form of intelligence. Various tests and assessments have been developed to evaluate different aspects of emotional intelligence, which provide scores for each branch of emotional intelligence as well as an overall score (Mayer et al., 2003).
In recent years, there has been a growing interest in evaluating the emotional intelligence of large language models (LLMs). Early efforts primarily focus on atomic tasks in emotion recognition, such as
aspect-based sentiment analysis, target-dependent sentiment classification (Tang et al., 2015), etc. As the capabilities of dialogue models advanced, evaluations expand to include sentiment analysis in a conversational setting (Li et al., 2023a), such as whether an LLM generate empathetic responses in a dialogue (Zhao et al., 2023). However, the overall assessment of emotional intelligence in LLMs is still lack of investigation (Schaaff et al., 2023), and existing evaluations often suffer from subjectivity issues (Elyoseph et al., 2023). Therefore, it’s necessary to construct a unified and objective benchmark for evaluating the emotional intelligence of LLMs, thereby enhancing their capabilities in handling real-world emotional interactions. For example, Fig. 1 shows two types of responses in different scenarios in the real world: blue for good responses and orange for bad ones. Good responses demonstrate empathy, concern or positive reinforcement, like asking about someone’s health outside a hospital or acknowledging hard work. Poor responses either miss the emotional context, like commenting on food when the user comes back from a hospital, or provide a possibly stressful choice, like asking someone to balance excitement and worry about a promotion. These scenarios pose a higher-level
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emotion requirements which are more discriminative for evaluate the emotion intelligence of LLMs.
Inspired by the scenarios in the real world, we propose an evaluation benchmark named EmotionQueen to evaluate LLMs’ emotion intelligence in our work. Specifically, we construct 10,000 users’ statement including five categories and introduce four distinct tasks including Key Event Recognition, Mixed Event Recognition, Implicit Emotional Recognition, and Intention Recognition. LLMs are requested to recognize the most significant event or multiple events of similar importance expressed in the users’ statement, identifying underlying users’ deep emotions or intentions. They are also asked to make empathetic responses aiming at key events, or provide emotional support. Moreover, we introduce two metrics, including the PASS rate and the WIN rate to quantify LLMs’ capabilities in recognition and response for emotion-related statements. Experiments demonstrate that Claude2 and LLaMA-70B have great performance in EmotionQueen. In summary, our study makes three pivotal contributions:
• We propose a benchmark named EmotionQueen including four distinct tasks for evaluating LLMs’ emotion intelligence.
• Based on this benchmark, we introduce PASS rate and the WIN rate to quantify LLMs’ capabilities in recognition and response for emotion-related statements.
• We conduct comprehensive experiments for powerful LLMs, yielding significant conclusions about their capabilities and limitations in emotion intelligence.
2 Datasets and Task Setups We categorize the empathy benchmark into four distinct tasks: Key Event Recognition, where LLMs are expected to identify the more significant event in a user’s statement that includes both a significant event and a routine event. Mixed Event Recognition, where LLMs are tasked with simultaneously responding to both aspects when the user’s statement contains two events of similar importance. Implicit Emotional Recognition, focusing on LLMs identifying underlying deep emotions in the user’s statements. Intent Recognition, where LLMs should comprehend the user’s real purpose and provide specific suggestions, going beyond just offering com-
fort. Based on the aforementioned four dimensions, we concentrate on generating 10,000 statements across five primary life scenarios using GPT-4 1, with an equal number of statements for each scenario. These scenarios are achievements, family and friends, health status, economic status, and accidents. Some statement examples are displayed in Table 14 (positive statement), and figures from Fig. 8 to Fig. 21 (negative statement). The portion of positive and negative statements are 3:7 in our generated statements, which caters for the real situation that positive statements often require less complex emotional expressions compared to negative ones. The tasks’ overview is depicted in Fig. 2, and the user statement generation prompts are detailed in Table 13.
2.1 Task setups
Key Event Recognition focuses on identifying and understanding significant events expressed by users and their emotional impact. Based on the Emotion-Focused Theory in psychology developed by Greenberg (2004), this approach encourages awareness and expression of emotions, including those neglected or not fully understood, and supports emotional transformation through exploring and processing emotional experiences. For instance, when a user’s statement contains multiple points of information (usually a significant event and a routine event), the LLM should identify the most significant event and ask questions based on it. For example, if a user says, “I ran into an old middle school classmate on my way to buy medicine,” the LLM should inquire about the reason for buying medicine rather than the meeting with the classmate. The LLM does not need to address both aspects simultaneously, meaning it should not respond to both the medicine purchase and the classmate encounter.
Mixed Event Recognition focuses on responding to both aspects simultaneously when the user’s statement includes two events of similar importance, differing from Key Event Recognition, which deals with a single important event. Based on the concept of emotional complexity developed by Lindquist and Barrett (2008), a statement usually conveys multiple events with multi-aspect emotion at similar importance, and an empathetic response is expected to pay attention to all involved events. Given a sample statement “I got promoted,
1https://chat.openai.com/
but this means I have to work further away from home,” a more empathetic response is “Are you excited about the promotion, and are you also worried about being far from home?” instead of ignoring any one event.
Implicit Emotional Recognition is about identifying underlying deep emotions. In some scenarios, although the user’s statement includes only one event, the emotion is implicit rather than directly expressed through language. The theory of Emotion Understanding, proposed by Mayer and Salovey (1993) and popularized by Cherniss and Goleman (2000), emphasizes recognizing both direct and implicit emotions expressed by others and providing appropriate responses. Therefore, it’s empathetic to identify implicit emotions behind users’ statements and provide targeted emotional support. For example, the response “Do you feel overwhelmed? Have you try some ways to relax?” is more suitable for emotional support than a simple reply “Hard work!” which seems perfunctory for responding to the statement “I’ve been busy with work all day.”
Intention Recognition aims to understand the potential purpose or needs behind users’ statements and provide specific help or solutions. Based on the pragmatics theory proposed by Grice (1978), especially the concept of Implicature, this approach suggests that language communication involves understanding not only the literal meaning but also the speaker’s intent and purpose. For a sample statement like “I’ve been busy with work all day,” a response that recognizes intention is “Have you eaten? I can order some takeout for you.” instead of a simple emotional support like “Remember to eat.”
2.2 Evaluation metrics
We propose employing PASS rate and WIN rate to evaluate four tasks using GPT-4, where each LLM response is rated as either 0 or 1, disregarding ambiguous middle results. Because it can be quite challenging to measure the difference between intermediate levels such as 3 or 4 objectively in 1-5 scale. The PASS rate assesses an LLM’s accuracy in recognizing emotion-related events, while the WIN rate evaluates its ability to provide an empathetic response.
In the Key Event Recognition task, if an LLM correctly identifies a key event, it earns 1 point in PASS, otherwise 0; if it also presents an empathetic response for that event, it scores 1 in WIN, otherwise 0. For instance, if a user says, “I visited my sick mother in the hospital today, then went to the supermarket,” and the LLM correctly recognizes “visiting the sick mother in the hospital” as the key event, it scores 1 in PASS. If the LLM responds with “Is your mother okay?”, it is deemed appropriate and scores 1 in WIN. Responses like “It’s troublesome that your mother is sick, hope she doesn’t keep you too busy” or “Everyone’s mother gets sick eventually” score 0.
In the Mixed Event Detection task, if an LLM recognizes both mentioned events, it scores 1 in PASS; if it provides an empathetic response to both, it scores 1 in WIN. For example, if a user mentions, “I got promoted but also need to move to a new city,” and the LLM identifies both “got promoted” and “move to a new city” correctly, it scores 1 in PASS. An LLM response like “Congratulations on the promotion! Is moving to a new city a challenge for you?” would score 1 in WIN, whereas focusing only on one aspect, like “Great, you got promoted! That’s a huge achievement,” scores 0.
For the Implicit Emotion Recognition task, if an LLM accurately identifies the core emotion in a user’s statement, it earns 1 in PASS; a corresponding empathetic response earns 1 in WIN. For instance, if a user says, “I’ve been feeling a bit anxious lately,” and the LLM correctly identifies “anxiety,” it scores 1 in PASS. A response like “Would you like to talk about what is making you anxious?” would be appropriate, scoring 1 in WIN, whereas a generic response like “Everyone gets anxious” scores 0.
In the Intent Detection task, if an LLM accurately discerns the core intent of a user’s statement, it scores 1 in PASS; offering specific advice or help earns 1 in WIN. For example, if a user states, “I am considering changing jobs recently,” and the LLM correctly identifies the intent, such as “offering career advice” or “helping find new job opportunities,” it scores 1 in PASS. An LLM response like “I can help you analyze the pros and cons of changing jobs, or offer some advice” would be appropriate, scoring 1 in WIN, while a response like “You should find suitable job opportunities yourself ” scores 0.
3 Experiments In this section, we conduct extensive experiments to evaluate different LLMs’ performance in the proposed EmotionQueen.
3.1 Experimental Setups
Our experiments are conducted on 8 Nvidia A100 GPUs, each with 80GB of memory, and we use PyTorch 2 in Python 3. We set the maximum sequence length for both input and output sequences to maximum 100 tokens, ensuring the responses not over lengthy. We also conduct an analysis of the average response length from LLMs and find it to be 52.3 tokens, demonstrating the reasonableness of the set sequence length.
3.2 Datasets, Baselines and Metrics
The baseline LLMs for this evaluation are BLOOM7B (Workshop et al., 2023) BLOOM-176B (Workshop et al., 2023), Claude2 (Bai et al., 2022), Falcon-7B (Almazrouei et al., 2023), Falcon180B (Almazrouei et al., 2023), GPT3.5 (Brown et al., 2020), GPT4 (OpenAI, 2023), LLaMA27B (Touvron et al., 2023), LLaMA2-70B (Touvron
2https://pytorch.org/ 3https://www.python.org/
et al., 2023), Vicuna-7B (Chiang et al., 2023), and Vicuna-33B (Zheng et al., 2023b).
We randomly select 1000 questions each task generated by each LLM and enroll three volunteers to manually evaluate generated responses with the same metrics as GPT4. Our annotators are selected from a pool of female graduate students in psychology. While gender may not necessarily impact the scoring, there’s a common perception that women tend to be more detail-oriented in their thinking, especially regarding emotional intelligence. We first inform the annotators about the intent of each task and the rules for scoring. Then, we ask them to score the responses. To ensure the reliability and confidence of human ratings, we also calculate Inter-rater agreement of Krippendorff’s Alpha (IRA) to ensure the confidence of human ratings. For the controversial ratings which have low agreements (less than 0.7), we discard this statement and introduce another one.
3.3 Main results Question 1:Which LLM is the winner of the EmotionQueen? Answer 1: LLaMA-70B! But Claude2 is slightly fall behind!
Performance of different LLMs across four tasks is shown in Table 1, Table 2, Table 3 and Table 4. We rank them based on the average of PASS rate and WIN rate, and compare the their comprehensive empathy capabilities as shown in Table 5
and Fig. 3. In the aspect of key event recognition (denoted as “KER”), Claude2, BLOOM-176B, and LLaMA2-70B show nearly perfect PASS rate, demonstrating their strong capability in capturing the core events of user statements. In WIN rate, Claude2 maintains a lead, other LLMs such as Vicuna-33b and LLaMA2-70B fall slightly short. In the aspect of mixed event recognition (denoted as “MER”), Claude2 also excels in identifying multiple events, showing its capability in handling complex scenarios. But the WIN rate generally decreases for all LLMs, suggesting potential room for improvement in understanding and balanced responses to mixed events. Regarding implicit emotion recognition (denoted as “IER”), LLaMA2-70B, Vicuna-33B, and GPT4 perform well. However, the WIN rate analysis shows that, except for LLaMA270B, other LLMs like Vicuna-33B and GPT4 have gaps in response appropriateness. In intent recognition (denoted as “IR”), most LLMs, especially Claude2 and LLaMA2-70B, show a high PASS rate, but they still show potential in providing effective guidance. Overall, although most LLMs excel in identifying the key content of user statements, they still have room for improvement in providing empathetic responses.
We also explore the emotional intelligence of these LLMs on the traditional emotion datasets, including MELD (Poria et al., 2018), Emotion-
Lines (Chen et al., 2018), and DailyDialog (Li et al., 2017), which are all about emotion categorization. We randomly select 100 samples from each for assessment with accuracy as the metric. We find that the results as shown in Table 6 resemble the rankings in Implicit Emotion Recognition’s PASS, suggesting that our tasks, inclusive of these emotion categorization tasks, mirror the challenge of recognizing implicit emotions. This confirms that our task setup effectively encompasses these types of emotional recognition challenges.
Moreover, in-context learning (ICL) which introduces a human-written sample into the prompt typically improves LLMs’ performance as shown in Table 7, while most LLMs show a decline in the 0-shot setting, demonstrating its critical role in enhancing the quality of recognition and response.
Question 2: What’s the relationship between PASS and WIN rate? Answer 2: There is almost no correlation!
We also analyze the relationship between PASS and WIN rate in four tasks as shown in Fig. 4. It suggests that there is little correlation between the PASS and WIN rate across different LLMs for the various tasks. In the Key Event Recognition task, most LLMs cluster around high PASS scores and high WIN scores, indicating their strong capabilities in this task. In the Mixed Events Recognition task, when observing a low WIN rate (below 30%), there appears to be a positive correlation between PASS and WIN rate, with a wide dispersion in WIN rates among the LLMs. However, Claude2 still stands out, achieving high PASS rate and WIN rate compared to others. In Implicit Emotion Recognition and Intent Recognition tasks, there’s virtually no correlation between PASS and WIN rates, with PASS rates being generally high across the board. For Implicit Emotion Recognition, WIN rates are distributed across various scores, whereas for Intent Recognition, there is a concentration of higher WIN rates. Overall, LLaMA-70B and Claude2 show consistent and superior performance across all tasks and metrics, while other models have their strengths in specific areas.
Question 3: Which LLM wins in each domain? Answer 3: LLaMA-70B in economic status and accidents, as well as Claude2 in achievements, family and friends, and health status!
We also compare the performance of different models in different categories of events as shown in Table 8, Table 9, Table 10 and Table 11. We also rank them based on the average of PASS rate and WIN rate. In the category of the achievement (denoted as “Ach”), Claude2, LLaMA2-70B, and BLOOM-176B all show a 100% pass rate, but Claude2 slightly leads in win rate. In the category of the health status (denoted as “H”), Claude2 continues to hold the highest average score, with LLaMA2-70B and BLOOM-176B also performing well. In the category of the family and friends
(denoted as “F”), Claude2 and LLaMA2-70B continue to lead, showing their ability to handle complex emotional issues related to close relationships. In the category of the economic status (denoted as “E”), LLaMA2-70B achieves the highest score. Claude2 and GPT4 also show strong performance, particularly in PASS rate. Finally, in the category of accidents (denoted as “Acc”), LLaMA2-70B leads significantly in WIN rate, indicating its ability to provide more accurate and effective responses in sudden and bad situations. We also shown the relationship between PASS rate and WIN rate in each scenario of event in Fig. 5. The trend is similar as that overall relationship as shown in Fig. 4
Question 4: Is the automatic scores generated by GPT4 agree with human perspectives? Answer 4: Yes, and the correlation coefficient reaches 0.991!
compare the consistency between automatic scores and human ratings as shown Fig. 6. The Pearson correlation coefficient achieves 0.991, indicating a strong alignment. We also recruit three volunteers to respond to these statements, selecting the one with the highest average score for WIN and PASS as the human-written performance. We find that some LLMs, such as LLaMA2-70B and Claude2, have already surpassed the real human in empathy capabilities, at least in the tasks in EmotionQueen. This indicates that some LLMs are capable of generating responses that can exceed the emotional value brought by human interactions.
3.4 Case Study
We shown some cases in Fig. 7 and more cases are shown in figures from Fig. 8 to Fig. 21.
For example, in key event recognition, Claude2
not only accurately captures the significant event of user getting their dream job but also further inquires about the user’s favorite aspects of the new job. Similarly, in mixed event detection, Claude2 can simultaneously focus on the sadness of friends moving away and the content of the package. This demonstrates its capability to deeply understand user’s situation and effectively communicate. Looking further, in implicit emotion recognition, LLaMA2-70B not only identifies the underlying stress but also delves into the user’s feelings about the exams, providing emotional support. In intention recognition, addressing real-life problems of users, such as “My phone keeps dying...”, Claude2 offers practical advice, like reducing screen brightness, showing its capability to discern user needs and provide practical help.
There are also some bad cases from several LLMs as shown in Table 15. For example, GPT3.5 echoes the original statement without adding empathy in the key event recognition. GPT4 acknowledges the implied emotion but behaves more like questioning than offering support. Claude2 shows interest and asks for more details without acknowledging the difficulties mentioned. LLaMA-70B overlooks the potential concern about volcanic activity and focuses only on the travel inspiration
aspect. Vicuna-33B’s response is positive, asking for details but missing the frustration of waiting. Falcon-180B fails to address any sympathy for the experience at the police station, focusing instead on the choice of bread. BLOOM-176B invites further discussion with an open-ended question but doesn’t convey understanding for the difficulties. BLOOM7B’s casual greeting seems to ignore the gravity of the sadness. LLaMA2-7B acknowledges the friend’s kindness but might need more emotional resonance. Vicuna-7B’s response could lead to anxiety by focusing on negative outcomes. Falcon7B’s could be perceived as accusatory, questioning the user’s intent without offering consolation. In the future, enhancements could be made through methods like supervised fine-tuning or reinforcement learning to improve LLMs’ performance in this benchmark.
4 Related Work
4.1 Emotion recognition
LLMs has seen significant advancements in emotion recognition. For example, Li et al. (2022) introduce the BiERU for conversational sentiment analysis; Wake et al. (2023) adopt ChatGPT to recognize emotions from text; Feng et al. (2023) study the capability of LLMs in recognizing human affect in the conversation; Li et al. (2023a) introduce a new emotion task called conversational aspect-based sentiment quadruple analysis; Mao et al. (2023) conduct an empirical study on promptbased sentiment analysis and emotion detection; Huang et al. (2023) shows modalities perception and adaptive learning in diverse contexts; Lei et al. (2023) recreates emotion recognition by leveraging LLMs in conversational settings; Zhang et al. (2023b) investigate the performance of LLMs in emotion recognition; Sun et al. (2023) propose a multi-LLM negotiation framework for sentiment
analysis; Zhang et al. (2023a) provide a investigation of LLMs’ capabilities in various sentiment analysis tasks. However, even when emotions are accurately recognized, these tend to be explicit emotions. In the real world, implicit emotions are prevalent.
4.2 Empathetic dialogue
LLMs are demonstrated have great performance in empathetic dialogues. For example, Zhao et al. (2023) assessing ChatGPT’s performance in understanding and generating emotional dialogue; Lee et al. (2022) explore empathetic dialogue generation with GPT-3; Li et al. (2023b) delve into LLMs’ understanding of and response to emotional stimuli in the conversation; Qian et al. (2023) explore the use of LLMs for generating empathetic dialogue responses; Zheng et al. (2023a) address the dialogue augmentation challenge in emotional support conversation; tse Huang et al. (2023) propose EmotionBench to evaluate LLMs’ empathy by assessing their emotional responses to specific situations; Zheng et al. (2023c) discuss the challenges in building emotional support chatbots and create a emotional support dialogue dataset; Tian et al. (2023)introduce ChatPLUG, a Chinese opendomain dialogue system enhanced; While it is acknowledged that current LLMs possess a certain degree of capability for emotional dialogue, there is not a comprehensive benchmark that evaluate LLMs’ emotion intelligence in handling more complex emotion-related scenarios.
5 Conclusions and Future Work In conclusion, our study introduces a novel framework named EmotionQueen to evaluate the emotional intelligence of LLMs. We design four emotion-focused tasks, including Key Event Recognition, Mixed Event Recognition, Implicit Emotional Recognition, and Intention Recognition, ask-
ing LLMs to recognize and respond to emotional intentions in user statements. Our findings underscore Claude2 and LLaMA-70B achieve great performance in EmotionQueen, providing significant insights about LLMs’ capabilities and limitations in emotion intelligence. Looking to the future, we aim to extend the framework to capture an even broader spectrum of emotional intelligence facets with more robust metrics for precisely evaluating emotion intelligence of LLMs.
Limitations The limitations of our study pertain to two main areas. First, LLMs may generate harmful or inappropriate utterances, which may not be discovered by our evaluation metrics. Despite efforts to mitigate such outputs, the complex nature of language and context can lead to scenarios where the LLMs’ responses are offensive or ethically dubious. Second, while our evaluation framework is designed to assess the emotional intelligence of LLMs, it is not immune to subjectivity. The interpretation of emotional events and the judgment of the quality of responses can be influenced by the personal biases of the assessors. In light of these limitations, future research must focus on enhancing the ethical safeguards of responses of LLMs and on refining the objectivity of emotional intelligence evaluations.
Acknowledgements This work is supported by Science and Technology Commission of Shanghai Municipality Grant (No. 22511105902), Shanghai Municipal Science and Technology Major Project (No.2021SHZDZX0103), the National Natural Science Foundation of China (No.62072323), Shanghai Science and Technology Innovation Action Plan (No. 22511104700), and the Zhejiang Lab Open Research Project (NO. K2022NB0AB04).
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Task Statement Response
Key Event Recognition To welcome the birth of my child, I start learning to sing lullabies. Oh my, you’re about to have a child! Congratulations!
Mixed Event Recognition I hear the news about the marriage of my close friend, and then I discover that the flowers I planted in my garden are bloomed. I’m so happy for your friend! and what kind of flowers do you plant?
Implicit Emotion Recognition I saw a beautiful sunrise this morning. That sounds amazing! Did it make you feel peaceful or inspired?
Intention Recognition I just got promoted at work! Congratulations on your promotion! That’s great news. What can I help you with now that you’ve taken on a new role?
Table 14: Responses of LLMs in positive scenarios.
LLM Task Statement Response
GPT3.5 Key Event Recognition Due to the unexpected medical bills, our family’s annual vacation has been downsized to a weekend road trip. A weekend road trip?
GPT4 Implicit Emotion Recognition I keep forgetting to water my plants. Is it hard to keep up with your daily routine?
Claude2 Mixed Event Recognition While grappling with the reality of pay cuts, I helped my neighbor fix their fence. Thanks for sharing a bit about yourself. I’m curious to know more about how you helped your neighbor fix their fence. What prompted you to help them with that project?
LLaMA2-70B Mixed Event Recognition I hear about a volcano I once hiked is now showing signs of activity today, and then I flip through a travel magazine focused on serene destinations. Oh, that sounds like a great idea! I’m always looking for new travel inspiration. What caught your eye in the magazine? Did you come across any destinations that you’re considering for your next trip?
Vicuna-33B Key Event Recognition Following a long wait at the doctor’s office, I bought a novel from the bookstore next door. Oh, that’s interesting! What kind of novel did you buy, and have you started reading it yet?
Falcon-180B Key Event Recognition After spending hours at the police station, I was hungry so I stopped by the grocery store to pick up some bread. Oh, did you find what you were looking for at the grocery store? What kind of bread did you end up getting?
BLOOM-176B Mixed Event Recognition After a day of dealing with plumbing issues at home, I enjoyed a quiet dinner at my favorite restaurant. Can you share more about what you were feeling during that time?
BLOOM-7B Mixed Event Recognition On the day we received news of my uncle’s passing, I absentmindedly sorted through old photographs. Hey, what’s up?
LLaMA2-7B Intention Recognition I received a thoughtful gift from a friend. That’s really kind of them! Did you have any idea they were planning to give you something?

heading: Future Work
text: In the future, enhancements could be made through methods like supervised fine-tuning or reinforcement learning to improve LLMs’ performance in this benchmark. 4 Related Work
4. 1 Emotion recognition
LLMs has seen significant advancements in emotion recognition. For example, Li et al. (2022) introduce the BiERU for conversational sentiment analysis; Wake et al. (2023) adopt ChatGPT to recognize emotions from text; Feng et al. (2023) study the capability of LLMs in recognizing human affect in the conversation; Li et al. (2023a) introduce a new emotion task called conversational aspect-based sentiment quadruple analysis; Mao et al. (2023) conduct an empirical study on promptbased sentiment analysis and emotion detection; Huang et al. (2023) shows modalities perception and adaptive learning in diverse contexts; Lei et al. (2023) recreates emotion recognition by leveraging LLMs in conversational settings; Zhang et al. (2023b) investigate the performance of LLMs in emotion recognition; Sun et al. (2023) propose a multi-LLM negotiation framework for sentiment
analysis; Zhang et al. (2023a) provide a investigation of LLMs’ capabilities in various sentiment analysis tasks. However, even when emotions are accurately recognized, these tend to be explicit emotions. In the real world, implicit emotions are prevalent. 4. 2 Empathetic dialogue
LLMs are demonstrated have great performance in empathetic dialogues. For example, Zhao et al. (2023) assessing ChatGPT’s performance in understanding and generating emotional dialogue; Lee et al. (2022) explore empathetic dialogue generation with GPT-3; Li et al. (2023b) delve into LLMs’ understanding of and response to emotional stimuli in the conversation; Qian et al. (2023) explore the use of LLMs for generating empathetic dialogue responses; Zheng et al. (2023a) address the dialogue augmentation challenge in emotional support conversation; tse Huang et al. (2023) propose EmotionBench to evaluate LLMs’ empathy by assessing their emotional responses to specific situations; Zheng et al. (2023c) discuss the challenges in building emotional support chatbots and create a emotional support dialogue dataset; Tian et al. (2023)introduce ChatPLUG, a Chinese opendomain dialogue system enhanced; While it is acknowledged that current LLMs possess a certain degree of capability for emotional dialogue, there is not a comprehensive benchmark that evaluate LLMs’ emotion intelligence in handling more complex emotion-related scenarios