E motion Q ueen: 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.

## 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 @xcite . 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 @xcite . 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 @xcite , etc. as the capabilities of dialogue models advanced, evaluations expand to include sentiment analysis in a conversational setting @xcite , such as whether an llm generate empathetic responses in a dialogue @xcite . however, the overall assessment of emotional intelligence in llms is still lack of investigation @xcite , and existing evaluations often suffer from subjectivity issues @xcite . 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 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 emotion-queen 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 emotion-queen. in summary, our study makes three pivotal contributions: ## 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 foot_0 , 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 ## experiments in this section, we conduct extensive experiments to evaluate different llms' performance in the proposed emotionqueen.