MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues (2024)

Abstract: The advent of Large Language Models (LLMs) has drastically enhanced dialogue systems. However, comprehensively evaluating the dialogue abilities of LLMs remains a challenge. Previous benchmarks have primarily focused on single-turn dialogues or provided coarsegrained and incomplete assessments of multiturn dialogues, overlooking the complexity and fine-grained nuances of real-life dialogues. To address this issue, we introduce MT-Bench101, specifically designed to evaluate the finegrained abilities of LLMs in multi-turn dialogues. By conducting a detailed analysis of real multi-turn dialogue data, we construct a three-tier hierarchical ability taxonomy comprising 4208 turns across 1388 multi-turn dialogues in 13 distinct tasks. We then evaluate 21 popular LLMs based on MT-Bench101, conducting comprehensive analyses from both ability and task perspectives and observing differing trends in LLMs performance across dialogue turns within various tasks. Further analysis indicates that neither utilizing common alignment techniques nor chat-specific designs has led to obvious enhancements in the multi-turn abilities of LLMs. Extensive case studies suggest that our designed tasks accurately assess the corresponding multi-turn abilities. The data and code are available at https: //github.com/mtbench101/mt-bench-101.

heading: 1 Introduction
text: Large Language Models (LLMs) based chatbots have made remarkable advances and significantly enhanced dialogue systems. Several benchmarks have been introduced to assess the capabilities of Large Language Models (LLMs) in single-turn dialogues, e.g., MMLU (Hendrycks et al., 2020), BBH (Srivastava et al., 2022), and AlpacaEval (Li et al., 2023b). However, daily dialogues between users and chatbots usually involve multi-turn conversations (Gudibande et al., 2023; Zheng et al.,
∗ Equal contribution. † Corresponding author.
2023), which include multiple utterances as part of the dialogue history. Therefore, it is essential to evaluate the proficiency of LLMs in generating coherent responses utilizing multiple utterances (Lan et al., 2020). Early studies like MT-bench (Zheng et al., 2024) mainly focus on two-turn dialogues and coarse-grained abilities, not sufficiently covering the complexity of real-world multi-turn dialogue scenarios. This indicates a considerable scope for improvement in current benchmarks for multi-turn dialogues, underscoring the urgent need to develop a comprehensive benchmark that can effectively compare the chat abilities of LLMs in multi-turn dialogues.
In this paper, we introduce MT-Bench-101, a new benchmark designed specifically for evaluating the chat capabilities of LLMs in multi-turn dialogues, as shown in Figure 1. During the ability modeling process for multi-turn dialogue, we undertake a systematical analysis combining realworld multi-turn dialogue data (Gudibande et al., 2023; Zheng et al., 2023) with the teaching taxonomy from educational psychology (Alexander, 2018; Marchel, 2007). This integrated approach
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has culminated in the formulation of a three-tier hierarchical ability taxonomy which is both datadriven and rooted in psychological frameworks.
Figure 2 illustrates the overall framework of our ability taxonomy. The first layer outlines three progressive overarching abilities which are depicted in Figure 1. Perceptivity is the most fundamental ability, reflecting the model’s accuracy in understanding context. Adaptability is built upon this foundation, indicating the model’s ability to respond effectively to user feedback. Finally, Interactivity captures the capacity of models for proactive engagement with humans, which is crucial for excelling in multi-turn interactions. The second tier specifies seven detailed abilities, while the third tier further decomposes these abilities into 13 distinct tasks. This taxonomy provides evaluation results across three levels from general to detailed, allowing for the identification of deficiencies in models at varying levels of granularity. For each third-tier task, we meticulously design specific prompts and utilize GPT-4 for data generation. In total, MTBench-101 encompasses 4208 turns within 1388 multi-turn dialogues.
For evaluation, we utilize golden context as dialogue history allowing LLMs to generate more fluid and rational dialogues, and we follow Zheng et al., 2024; Dubois et al., 2024; Duan et al., 2023 to utilize GPT-4 as a stand-in for human raters to score for each turn. We design unique scoring guidelines for each task and use the lowest round score as the total score for the dialogue to allow for a more rational assessment.
We then perform extensive experiments on MTBench-101 to assess the multi-turn chat ability of existing LLMs, including 2 close-sourced LLMs and 19 open-sourced LLMs. Our findings include:
• We identify adaptability and interactivity as the key deficiencies of existing LLMs, and GPT-4 is the most powerful model for multi-turn dialogues.
• The average performance of models within various tasks exhibits differing trends with the progression of turns, reflecting the distinct characteristics of the abilities.
• Model performance improves as the model size increases. However, neither utilizing common alignment techniques (such as RLHF) nor chatspecific designs has resulted in significant enhancements in the multi-turn abilities of LLMs.
• The agreement between GPT-4 and human expert evaluations reached 87%, utilizing our designed evaluation approach.

heading: 2 Related Work
text: LLMs for Multi-turn Dialogues Recent advancements in LLMs such as GPT-3.5/GPT4 (Ouyang et al., 2022; OpenAI, 2022, 2023) have garnered significant attention (Liu et al., 2024a; Wang et al., 2023d; Feng et al., 2022; Bu et al., 2021; Peng et al., 2023; Sun et al., 2024; Guo et al., 2024b, 2023; Lyu et al., 2024). To enhance the multi-turn capabilities of open-sourced LLMs, initial efforts begin with collecting humanChatGPT dialogues (Gudibande et al., 2023), leading to the creation of Vicuna (Chiang et al., 2023). RealChat (Zheng et al., 2023) later expands the data to 1 million conversations. To generate more sophisticated datasets, Baize (Xu et al., 2023) and UltraChat (Ding et al., 2023) employ alternating GPT interactions. Parrot (Sun et al., 2023) trains a model to simulate the user, thereby generating improved data. Further, Cue-CoT and ICL-AIF (Wang et al., 2023a; Fu et al., 2023) enhance model capabilities for multi-turn interactions through In-ContextLearning (ICL) (Brown et al., 2020) and Chain-ofThought (CoT) (Wei et al., 2022) algorithms.
Benchmarks for Multi-turn LLMs Most benchmarks evaluate LLMs through single-turn instructions (Hendrycks et al., 2020), missing the nuance of human conversation. To investigate multi-turn ability, ABC-Eval (Finch et al., 2022) relies on labor-intensive human evaluations. AlpacaEval (Li
et al., 2023b) and PandaLM (Wang et al., 2023c) attempt to automatically assess open-ended instructions, but they remain confined to single-turn settings. MT-Bench (Zheng et al., 2024) and MTBench++ (Sun et al., 2023) expand multi-turn evaluations across eight topics. BotChat (Duan et al., 2023) and MINT (Wang et al., 2023b) focus on specialized tasks such as dialogue generation abilities. Despite these efforts, there remains a notable gap in fine-grained evaluations for multi-turn interactions.
Benchmarks for Fine-grained Abilities The advent of general LLMs has highlighted the need for more comprehensive evaluation. Hendrycks et al., 2020 introduces MMLU with an extensive suite of 57 tasks spanning social science and STEM. Subsequent benchmarks (Huang et al., 2024; Li et al., 2023a; Zhong et al., 2023; Srivastava et al., 2022; Yu et al., 2023; Li et al., 2024b; Guo et al., 2024a) aim to rigorously assess LLMs’ knowledge and logic. ConceptMath (Wu et al., 2024) and FollowBench (Jiang et al., 2023b) develop a hierarchical framework for evaluating model capabilities.

heading: 3 MT-Bench-101
text: This section begins with a detailed description of the three-tier hierarchical ability taxonomy for multi-turn dialogues. Following that, we explain the methodology used to collect the dataset. Finally, we present an analysis of the dataset’s statistics.

heading: 3.1 Hierarchical Ability Taxonomy
text: After analyzing real dialogues from ShareGPT (Gudibande et al., 2023) and RealChat (Zheng et al., 2023) and the teaching taxonomy of multi-turn dialogues from educational psychology (Alexander, 2018; Marchel, 2007; Peng et al., 2020; Gao et al., 2018), we have developed a hierarchical taxonomy of abilities crucial for chatbots to engage effectively in multi-turn dialogues with human users. This taxonomy is structured into three levels, with the third level encompassing 13 distinct tasks. Table 1 provides a brief one-sentence description for each third-level task. This section will deliver a detailed explanation of these three-level abilities and tasks. We also provide cases for each task in the Appendix F.

heading: 3.1.1 Perceptivity
text: Perceptivity requires chatbots to adeptly track and use historical dialogue data to provide logical and consistent responses, encompassing the following three core abilities.
Context Memory: To ensure continuity and relevance in dialogues, chatbots must exhibit a robust context memory capability. This involves accurately retrieving and utilizing past dialogue information to address current user inquiries. We named Context Memory (CM) for the third-level ability.
Context Understanding: Anaphora Resolution (AR). It is common for users to use demonstrative pronouns (e.g., "these," "it") in dialogues. A key ability for chatbots is to identify the referents of these pronouns accurately to generate appropriate responses; Separate Input (SI). Dialogues typically unfold over several turns, with the initial turn outlining the task requirements and subsequent turns specifying the task input. Understanding the relationship between instructions and inputs is demanded for effective chatbots.
Context Interference: Topic Shift (TS). Users might unpredictively switch topics in multi-turn dialogues. This task assesses the chatbot’s ability to recognize a topic shift and ignore unrelated preceding information, thereby concentrating on the new topic at hand; Content Confusion (CC) centers on the chatbot’s skill in managing situations where users pose questions that, while textually similar in history questions, necessitate distinct responses.

heading: 3.1.2 Adaptability
text: Chatbots adjust their early responses with the user’s new requirements (Rephrasing), new conditions, and hypotheses (Reasoning) and can correct or insist on answers according to user-challenging feedback (Reflective) in user-triggered dialogue.
Rephrasing: Content Rephrasing (CR) requires chatbots to have a thorough understanding of the text to rephrase the content of the last response based on the user’s latest requirement (e.g., summarizing this paragraph). Format Rephrasing (FR) involves a transformation in structure while preserving the original information (e.g., converting this paragraph into a list format).
Reflection: Self-correction (SC). Upon receiving user feedback indicating skepticism or errors in the last response, the chatbot will correct mistakes, and provide a more accurate subsequent response. Within our dataset, this task is limited to instances where the chatbot’s initial reply was erroneous or not precise, and the user’s critique is deemed valid. Self-affirmation (SA). Unlike self-correction task, self-affirmation comes into play when the chatbot’s initial response is correct or accurate, yet it encoun-
Benchmark #Dialogues #Turns #Tasks Fine-grained
AlpacaEval 805 805 1 ✗ MT-Bench 80 160 1 ✗ MT-Bench++ 80 640 1 ✗ BotChat 547 547 1 ✗ MINT 568 568 3 ✗
MT-Bench-101 1388 4208 13 ✓

heading: 3.1.3 Interactivity

heading: 3.2 Data Collection
text: We tailored unique data generation prompts for each task based on its specific characteristics and utilized GPT-4 to construct data. In detail, the prompts included data generation rules and used manually crafted examples as guidance for GPT4. This ensured the generated data met the specific needs of each task. Our benchmark covers 30 diverse topics, including health, history, science, finance, law, humanities, arts, and others. The Appendix A shows a complete list of topics and the prompts used for data generation.
For each task, we utilized GPT-4 to generate over 1000 samples. These samples were then rigorously curated by human annotators to form the final dataset. For each piece of data, it underwent screening by five annotators, and we ultimately retained only the data that all annotators deemed to be of high quality. The primary criteria for curation are shown in the Appendix A.

heading: 3.3 Data Statistics
text: Table 2 shows the key statistics of our MT-Bench101. This benchmark features a comprehensive hierarchical taxonomy for multi-turn dialogues with 13 distinct tasks, 1388 dialogues, and 4208 turns. Detailed statistics for each task can be found in the Appendix B. Additionally, we provide a comparative analysis between MT-Bench-101 and existing dialogue evaluation benchmarks. This comparison highlights that MT-Bench-101 is the first dataset to specifically focus on fine-grained multi-turn dialogue abilities, notable for its extensive volume of data and diversity of tasks.

heading: 3.4 Evaluation
text: In multi-turn dialogues, new turns rely on the interaction between humans and chatbots in the
preceding turns. This phenomenon is especially significant in tasks with strong interactivity such as instruction clarification and proactive interaction. Hence, we leverage our meticulously curated dataset as the golden context for dialogue history, as opposed to relying on self-predicted context from LLM subjects. This approach facilitates the creation of smoother, more rational dialogues. Moreover, evaluating only the newest response of the LLMs while maintaining consistency with the conversation history also promotes fair evaluation.
Following MT-Bench (Zheng et al., 2024), we employ GPT-4 for evaluation in our benchmark. We tailor different evaluation prompts (see Appendix C) for each task and develop fine-grained scoring guidelines detailing what is required for each score level or grade. Then GPT-4 scores each turn of the chatbot’s responses from 1 to 10 and gives detailed justifications. Additionally, our evaluation process utilizes a minimum-score-taking metric, where the lowest score of a turn is considered the final score for the entire dialogue. This approach is consistent with human intuition, as discussed in section 4.5, because a single failed response can compromise the entire dialogue in closely related conversational contexts. Moreover, this metric prevents models from achieving inflated scores by simply learning patterns from the golden context. This phenomenon will be further explored in section 4.2.
Li et al. 2024a; He et al. 2022 point out that there is self-bias in LLM judges (e.g., GPT-4 Judge prefers GPT-4 answers). We also provide a leaderboard with Qwen-72B-Chat as the judge model in the Appendix D, showing that this problem is minor in our benchmark, with the rankings of GPT4-Judge and Qwen-72B-Judge being consistent.

heading: 4 Experiments

heading: 4.1 Experimental Setup
text: Settings We utilize the golden contexts as dialogue histories in all experiments unless otherwise specified. For each LLM, we apply the corresponding chat format and the system prompt while setting the temperature to 0. Additional details on the experimental setup and implementation can be found in the Appendix C.
Models We evaluate 21 popular LLMs on MTBench-101, including 2 close-sourced LLMs (i.e., GPT-3.5/ GPT-4 (OpenAI, 2023)) and 19 opensourced LLMs (i.e., Llama2-Chat (7B, 13B) (Touvron et al., 2023), Mistral-Instruct (7B, 8x7B,
DPO) (Jiang et al., 2023a), Qwen-Chat (7B, 14B) (Bai et al., 2023), Yi-Chat (6B, 34B) (Yi, 2023), ChatGLM2-6B/ChatGLM3-6B (Du et al., 2022), InternLM2-Chat (7B, 20B, RLHF) (Team, 2023), Vicuna-13B-v1.5 (Chiang et al., 2023), Baichuan2-Chat-13B (Baichuan, 2023)), UltraLM13B-v2.0 (Ding et al., 2023), and Baize-v213B (Xu et al., 2023). More details of these evaluated models can be seen in the Appendix E.

heading: 4.2 Main Results
text: Task Dimensional Analysis Table 3 presents the performance of different language models on the 13 multi-turn dialogue tasks in our MT-Bench-101. Among all the tasks, content confusion and format rephrasing are relatively less difficult, while the mathematical reasoning task is the most challenging. Furthermore, closed-source models consistently exhibit superior performance compared to open-source counterparts across all evaluated tasks. GPT-4 emerges as the top-performing model across the entire spectrum of tasks with an average score of 8.86, while Yi-34B with an average score of 8.10 ranks as the second-best performer overall.
Ability Dimensional Analysis Table 3 further indicates that model performances across tasks within the same ability tend to be similar, inspiring us to assess the overall performance of various models from the perspective of the abilities. Figure 3 illustrates the performance of different LLMs across seven ability dimensions, where the score for each ability is the average score across its respective tasks. Most LLMs demonstrate a widespread proficiency in rephrasing and resistance to interference. However, the reasoning and questioning abilities of LLMs are still in need of enhancement. In addition, the performance of models in memory surpasses that in understanding ability. This discrepancy arises because memory is primarily concerned with the recall of information, whereas understanding encompasses the grasping of meaning, representing a deeper level of cognitive processing. Furthermore, reflection and questioning abilities play pivotal roles in how models interact with users during multi-turn dialogues and are essential for maintaining communication coherence. Consequently, models that excel in reflection and questioning not only show proficiency in individual tasks but also suggest a higher level of overall conversational intelligence and are often rewarded with higher overall scores.
Chat-Specific Models As shown in Table 3, the chat-specific language models Baize, and UltraLM do not demonstrate exceptional performance on our benchmark. In fact, their capabilities appear to be outstripped by other large language models of comparable size. Such insights indicate that despite being specialized for conversational tasks, these chat-specific models require further development to effectively handle the multi-turn scenarios.
Per-Turn Performance To investigate the impact of turn count on model performance across different tasks, we calculated the average scores of models for each dialogue turn within various tasks. As shown in Figure 4a and 4b, in content rephrasing, format rephrasing, context memory, and anaphora resolution tasks, the average performance of models show a decline between the first turn and subsequent turns. This suggests that in multi-turn dialogue tasks, models tend to exhibit a greater propensity to forget the content of previous turns or to develop comprehension biases as the conversation progresses. Figure 4b also illustrates a notable decrease in performance from the first to the second turn in topic shift and content confusion tasks. This drop is attributed to the second turn marking the onset of interference, leading to confusion for the model. As shown in Figure 4c, we note an upward trend in model performance as the number of turns increases in separate input, directive clarification, and proactive interaction. This phenomenon does not reflect a true enhancement in performance throughout the dialogue. Rather, it occurs because using the golden context as historical
information allows the model to learn the current conversational style and response patterns from the golden context, resulting in an illusory improvement in performance. This phenomenon will be analyzed in detail below. Similarly, as shown in Figure 4d, in mathematical reasoning tasks, the model also benefits from the golden context by adopting the reasoning format and solution paradigms (such as the step-by-step paradigm). Conversely, in general reasoning tasks, where there is no fixed paradigm to follow, the model’s performance tends to decline as the dialogue progresses due to the increasing complexity.

heading: 4.3 Further Analysis
text: Effect of Model Size Figure 5 presents a comprehensive comparison across four groups of models varying in size. The trend of increasing model size is associated with a universal improvement in per-
formance on multi-turn dialogue tasks. Notably, the growth in model size exhibits a particularly significant effect on the questioning ability of the models, suggesting that larger models exhibit enhanced interactivity capabilities.
Effect of Human Preference Alignment Several techniques (Ouyang et al., 2022; Rafailov et al., 2024; Zhou et al., 2023, 2024a,b; Liu et al., 2024b) have been proposed to align language models with human values. We study the effect of RLHF/DPO on multi-turn dialogues by comparing three pairs of open-source models, each pair consisting of versions trained with Supervised Fine-Tuning (SFT) and enhanced with RLHF/DPO techniques. Table 4 shows that the application of RLHF/DPO techniques results in marginal improvements for the InternLM2-Chat models, with the 7B and 20B versions experiencing score increases of 0.16 and 0.10, respectively. In contrast, the Mistral-7B model shows a performance decrease of 0.06. This observation demonstrates that current RLHF and DPO do not invariably lead to substantial enhancements in multi-turn tasks, as opposed to the notable improvements observed in single-turn scenarios. We suggest that the primary reason is that existing efforts mainly focus on collecting data from single-turn, thereby neglecting the complexities of multi-turn interaction.
Effect of the Golden Context We employ ChatGLM3-6B on separate input and instruction clarification tasks with the golden context or self-
predicted context as historical conversational information to evaluate the effect of the context. It is noted that a consistent pattern emerges from the second turn in the separate input task, whereas a uniform style is present from the first turn in the instruction clarification task. Figure 6 shows that using the golden context as historical information leads to an increase in the model’s scores overturns. This improvement is attributed to the golden context supplying the model with data for in-context learning, enabling it to learn the specific patterns and styles from context. Conversely, employing self-predicted context as dialogue history results in the accumulation and propagation of errors from earlier incorrect responses, causing a gradual decline in scores. Furthermore, as discussed in section 3.4, using self-predicted context as historical information compromises the coherence of the dialogue, Due to these observed phenomena, our evaluation protocol employs the golden context and the lowest score across the turns as the metrics for assessing overall dialogue performance.

heading: 4.4 Case Study
text: We provide some cases to show that GPT-4 evaluation aligns well with the corresponding abilities and scoring criteria. Figure 7 shows that in separate input, LLMs frequently encounter two types of errors: (1) Generating premature responses without having received the detailed content of the task, and (2) Forgetting the initial requirements of the task, resulting in responses that stray from the original task objectives. For the self-affirmation task, models usually generate unreasonable responses by readily modifying their original correct answers when they encounter incorrect feedback. Detailed case studies for the remaining tasks can be found in the Appendix F.

heading: 4.5 Human Evaluation
text: We randomly sampled 100 dialogues from MTBench-101 and recruited five expert human annotators to assess the overall quality of multi-turn dialogues based on whether the responses of LLMs met the requirements of the corresponding tasks. Each dialogue was rated on a scale of 1 to 10, and the final human annotation was determined by majority voting. We then adopt the agreement metric from Zheng et al., 2024 to verify our autoevaluation method’s effectiveness, which defines the agreement between two types of judges as the probability of randomly selected individuals (but
not identical) of each type agreeing on a randomly selected question. As shown in Table 5, utilizing our evaluation prompt and detailed scoring guidelines, which specify the criteria for each score level or grade, the agreement between GPT-4 and human expert evaluations reached 87%, even surpassing the internal agreement among human experts of 80%. Additionally, we found that eliminating scoring criteria or adopting average values instead of minimum values as scoring metrics led to reduced evaluation agreement with human experts. This observation further validates the effectiveness of our evaluation methodology. We also calculate the Fleiss’ Kappa (Scott, 1955), a statistical measure of inter-rater reliability, to further justify our conclusions in the Appendix G.

heading: 5 Conclusion
text: This paper introduces a comprehensive hierarchical taxonomy of multi-turn chat abilities based on existing human-LLMs interaction data and educa-
Evaluation Method Agreement ∆
Human Experts 80% 0%
MT-Bench-101 87% +7% w/o scoring guidelines 77% -3% w/o minimum values metrics 75% -5%

heading: 6 Limitations

heading: 7 Ethics Statement

heading: A Details on the Data Generation
text: Our dataset is generated encompassing a wide range of 30 topics, which are medicine, health, history, science, technology, digital, automotive, astronomy, geography, lifestyle, literature, politics, physics, chemistry, biology, finance, stocks, law, humanities, entertainment, music, gaming, fashion, film and television, celebrities, sports, art, computer science, environment, and psychology. This variety ensures that our data spans a multitude of diverse fields and areas of interest.
After generating preliminary data using GPT-4, we manually filtered the data samples by human annotators to form the final dataset. The primary criteria for curation are as follows:
1. Ensure that our dataset precisely adheres to the data generation rules outlined for each specific task.
2. Ensure that our dataset encompasses samples from 30 different topics, with a minimum of 10 distinct topics covered for each task.
3. Remove similar dialogues with only variations in several keywords.
4. Remove questions regarding real-time issues (such as today’s weather) and those involving upto-date knowledge after 2022.
5. Remove dialogues that contain common sense errors, offensive content, and any personal identity information.
Figures 8 to 20 show the prompts we utilize for data generation. When generating data for each task, we splice in a uniform initial prompt and a unique prompt for each task to ensure that the generated data matches our ability and task requirements.

heading: B Detials on Data Statistics
text: Table 6 demonstrates the statistics information for each task as well as the overall statistics of MTBench-101. Note that the number of words is calculated from the golden context in the dataset.

heading: C Details on Evaluation
text: Figures 21 to 34 show the prompts we utilize for evaluation. For each task, we concatenate a uniform initial instruction, unique evaluation prompts tailored to the specific task, and a consistent scoring format to ensure that the scoring criteria align with our task requirements.
It’s also noteworthy that in format rephrasing, content rephrasing, anaphora resolution, self-
affirmation, self-correction, and context memory tasks, models generate responses directly from the second turn of dialogue, utilizing the golden context from the first turn as historical dialogue information. This approach is adopted due to the task characteristics, where the assessment of the respective abilities begins from the second turn of dialogue. While models need to utilize the dialogue history from the first turn in subsequent turns, directly responding to the content of the first turn lacks practical significance.

heading: D Additional results utilizing Qwen-72B-Chat
text: We evaluated the top 5 models in our benchmark using an open-source Qwen-72B-Chat, and the results in Table 7 show that GPT-4 is still the most powerful model and the rankings of GPT-4-Judge and Qwen-72B-Judge are consistent. This also shows that Qwen-72B-Chat is a good alternative evaluator. It is open-source, free to use, and won’t be updated or taken down in the future.

heading: E Model Details
text: All details about the evaluated models are presented in Table 8.

heading: F More Cases
text: Figures 35 to 45 show cases corresponding to each task, each reflecting the classical error of the model response. These cases show that our task design can accurately assess the corresponding ability of LLMs.

heading: G Fleiss’ Kappa between GPT-4 and Humans
text: To evaluate the agreement between humans and GPT-4, we also provide Fleiss’ Kappa score, which is an inter-annotator agreement metric. Specifically, we compute (1) The Fleiss’ Kappa for the five raters; (2) The average Fleiss’ Kappa between GPT-4 and each individual rater; (3) The Fleiss’ Kappa between GPT-4 and the majority vote of 5 human annotators. (4) The Fleiss’ Kappa of GPT4 and humans over all annotations. As shown in Table 9, the agreement between GPT-4 and humans is still higher than that among humans.

heading: Future Work
text: It is open-source, free to use, and won’t be updated or taken down in the future