D ialog B ench: Evaluating LLM s as Human-like Dialogue Systems (2024)

Abstract: Large language models (LLMs) have achieved remarkable breakthroughs in new dialogue capabilities by leveraging instruction tuning,which refreshes human impressions of dialogue systems. The long-standing goal of dialogue systems is to be human-like enough to establish long-term connections with users. Therefore, there has been an urgent need to evaluate LLMs as human-like dialogue systems. In this paper, we propose DialogBench, a dialogue evaluation benchmark that contains 12 dialogue tasks to probe the capabilities of LLMs as human-like dialogue systems should have. Specifically, we prompt GPT-4 to generate evaluation instances for each task. We first design the basic prompt based on widely used design principles and further mitigate the existing biases to generate higher-quality evaluation instances. Our extensive tests on English and Chinese DialogBench of 26 LLMs show that instruction tuning improves the human likeness of LLMs to a certain extent, but most LLMs still have much room for improvement as human-like dialogue systems. Interestingly, results also show that the positioning of assistant AI can make instruction tuning weaken the human emotional perception of LLMs and their mastery of information about human daily life.

## introduction large language models (llms) @xcite @xcite have achieved remarkable breakthroughs by leveraging instruction tuning @xcite , especially unlocking new dialogue capabilities. such new dialogue capabilities empower humans to naturally interact with llms, which has refreshed human's impression of dialogue systems. the long-standing goal of dialogue systems requires llms to be sufficiently human-like to establish long-term connections with users by satisfying the need for communication, affection and social belonging. specifically, human-likeness generally covers the following fine-grained capabilities: correctly understanding the dialogue context, making reasonable use of relevant knowledge, detecting the user's emotions and personality when necessary, and finally generating friendly and reasonable responses that are coherent and consistent with the dialogue context @xcite . however, the heightened human likeness could not correspond to improved scores on existing llm benchmarks. existing llm benchmarks are mostly oriented to evaluate the llms' abilities for task completion as assistant ai, such as human-knowledge mastery @xcite @xcite or instruction following @xcite . however, these benchmarks do not focus on whether llms as dialogue systems are sufficiently humanlike to establish long-term connections with users. therefore, an in-depth evaluation benchmark of those abilities related to human likeness is essential for identifying the strengths and limitations of llms as multi-turn dialogue systems. the most ideal approach is to collect corresponding high-quality dialogues from real humans. however, most real-human dialogues, whether from social networks or open datasets, are likely to have been leaked during the pre-training of llms. to prevent the issue of "data leakage", the evaluation benchmark must contain new evaluation instances and be updated frequently. due to the difficulty of human writing, it is necessary to construct new human-human dialogues as evaluation instances automatically. inspired by @xcite and @xcite , we explore the use of gpt-4 as a surrogate for humans to generate massive evaluation instances. in this paper, we propose a novel dialogue evaluation benchmark with gpt-4 as data generator, dialogbench for short. since dialogues generated without restrictions may not involve commonsense use or emotional expression, we generate corresponding evaluation instances for different finegrained capabilities. to evaluate comprehensive abilities, we select 12 dialogue tasks. each task requires llms to possess at least one ability to perform it well. for each task, we prompt gpt-4 to generate evaluation instances. specifically, we first design the basic prompt based on widely-used design principles and further mitigate the existing biases to generate most of the available evaluation instances. afterward, we filter out detrimental evaluation instances via a filter mechanism. consequently, we construct english and chinese dialogue evaluation benchmarks towards human likeness. we conduct a comprehensive evaluation of 26 llms using dialogbench, including pre-trained and supervised instruction-tuning models. experimental results reveal that instruction tuning can improve the human likeness of llms. for supervised instruction-tuning models, top-tier models can handle a wide array of dialogue tasks, indicating the potential for developing llms into humanlike dialogue systems. however, we observe significant performance gaps between top-tier models and other llms, which suggests that their performance lags considerably. in addition, llms generally perform better at correctly understanding context but are relatively poor at perceiving emotions and personality. current llms also do not understand much about daily human life. this underscores the necessity for more efforts to enhance the abilities related to the human likeness of most llms. our contributions are summarized as follows: (1) we present dialogbench, a comprehensive benchmark to standardize the evaluation of llms as human-like dialogue systems. (2) we perform a thorough evaluation of 26 different llms using di-alogbench, uncovering a significant performance evaluation under diverse dialogue tasks. it illuminates the top-tier llm in human likeness and highlights dimensions for improvement. dialogue benchmarks. there are several benchmarks for evaluating dialogue capabilities @xcite @xcite . these benchmarks can be used to evaluate language models that have been fine-tuned on the corresponding training sets but cannot directly evaluate instruction-following llms. in addition, these previous benchmarks may have been leaked during the pre-training of llms. in contrast, dialog-bench contains new evaluation instances with natural language, which can be directly used to evaluate instruction-following llms and avoid data leakage. @xcite evaluates llms' multiturn instruction-following abilities, which focuses on assessing its alignment with human preference, rather than llms as human-like dialogue systems. recent researchers @xcite @xcite @xcite also focus on human-like characters of gpt-4 or chatgpt. however, our work holistically evaluates capabilities related to human likeness. llms for data generation. many recent researches @xcite @xcite @xcite also leverage gpt-4 for data generation, mainly using several training instances as fewshot examples to prompt the generation of more training instances. in contrast, our work leverages gpt-4 to generate new evaluation instances for constructing benchmarks without few-shot examples. ## dialogbench in this section, our goal is to generate evaluation instances using gpt-4. to this end, in section 3.1, we describe the selection of dialogue tasks. in section 3.2, we describe how to determine the ques- optimized prompt evaluation instances data filter gpt-4 for each task task selection coherence correctness consistency safety bias mitigation style bias position bias domain bias goal description prompt style input data contextual information basic prompt 12 dialogue tasks dialogbench high-quality evaluation instances tion type of evaluation instances, like generation questions or multi-choice questions, to effectively reflect the quality of llms as human-like dialogue systems. in section 3.3, we design the basic prompt as the input of gpt-4. in section 3.4, we describe the biases of the basic prompt and the corresponding solutions, along with introducing a filter mechanism to pick out high-quality data. the overall architecture of dialogbench construction is shown in figure 1 . ## experimental setup dataset statistics. we report the statistics of di-alogbench in llms to evaluate. as a systematic attempt to benchmark existing llms ( evaluation method. for the above llms, we use accuracy as the metric and adopt different evaluation methods. (1) pre-trained llms: each option content is independently scored by concatenating it with the instruction along with the given dialogue and question as a prompt and computing the probability of "option content". specifically, we calculate the perplexity of each option content and then choose the label corresponding to the option content with the lowest perplexity as the predicted answer. this evaluation method is consistent with the training method of pre-trained llms (i.e., next token prediction), stimulating the optimal performance of llms. (2) supervised instruction-tuning llms: we regard the given dialogue as the history of chatting between the user and the llm. in the current interaction turn, we concatenate the instruction, along with the question and all options to form an exact string as the user's question to the llm, and then the llm gives the option label. in implementation, we allow llms to output at most 256 tokens, and then extract the outputted label as the predicted answer. implementation details. we further describe the parameter settings for gpt-4 when generating data and llms to be evaluated. when using gpt-4 to generate evaluation instances, we set temperature to 1, presence_penalty to 0.6, frequency_penalty to 0, and other parameters to default for the api parameters of gpt-4. when evaluating llms, we set the temperature to 0, the presence_penalty to 0.6, and the frequency_penalty to 0 for chat-gpt and gpt-4. besides, the temperature is set to 0, max_new_tokens is set to 256, and other parameters to default for other open-source models. furthermore, the versions of chatgpt and gpt- type model correctness coherence consistency safety sf ic krg crg di mrg prg rc ed nli ds od overall ## main results overall and task-specific scores on english and chinese dialogbench are reported in pretrained llms. on this challenging benchmark, surprisingly we discover that some pretrained llms (e.g., llama2-70b in english di-alogbench and baichuan2-13b in chinese dialog-bench) have pretty good performances. for other pre-trained llms, there is still much room for improvement in those fine-grained capabilities related 2 https://pytorch.org/ 3 https://huggingface.co/ to the human likeness. we further observe that: (1) for correctness, most pre-trained llms can perform well on slot filling (sf) but are relatively poor on the other 3 tasks. (2) for personalization consistency, pretrained llms as a whole have good performances in emotion perception (ed), whereas poor performance in personality following (prg). for semantic consistency, the decent performance on dialogue summarization (ds) indicates that pretrained llms perform well in maintaining semantic alignment. however, it is still relatively difficult in scenarios that require one-step reasoning, as shown by the performance on dialogue nli. (3) for coherence, the average performance of llms on dialogue infilling (di) and multi-turn response generation (mrg) is relatively similar, and there is still much room for improvement. (4) for offensive detection (od), most pre-trained llms can empower a certain capability of offensive detection. overall, current pre-trained llms perform relatively well on correctness-related tasks and have greater room for improvement on tasks related to coherence and safety. for consistency-related tasks, pre-trained llms must be continuously optimized to possess corresponding high-order capabilities. supervised instruction-tuning llms. we begin by observing that gpt-4 presents the best performance, which represents the strongest capabilities as a human-like dialogue system. additionally, the instruction-tuning llms achieve higher scores than the corresponding pre-trained llms on most dialogue tasks (e.g., baichuan2-13b), suggesting that instruction tuning is an efficient means for improving the capabilities that llms as human-like dialogue systems should have. we further observe that: (1) for correctness, most llms perform relatively well on all 4 tasks, indicating that these llms have decent abilities to generate correct dialogues. (2) for personalization consistency, most llms perform unsatisfactorily. interestingly, most llms achieve inferior scores on emotion classification than the corresponding pre-trained llms, such as qwen-7b. it might be because the positioning of assistant ai enables instruction tuning to focus on the ability to complete tasks, abandoning the ability to perceive emotions. ## further discussion we probe llms' performance for different domains and validate the effectiveness of adjusting dialogue style and introducing filtering mechanisms. performance on different domains. we calculate the average accuracy of all supervised instruction-tuning llms on each domain, as shown in figure 8 . the detailed results are displayed in ## conclusion we present dialogbench, a systematically designed dialogue benchmark for evaluating llms as human-like dialogue systems. dialogbench includes 12 dialogue tasks to probe the capabilities related to human likeness for comprehensive evaluation. for each task, we prompt gpt-4 to generate evaluation instances. specifically, we design the basic prompt based on widely-used design principles and further eliminate existing biases to generate higher-quality instances. an extensive study of 26 llms, including pre-trained and supervised instruction-tuning, is conducted in chinese and english dialogbench. we unveil that instruction finetuning can improve the human likeness of llms to a certain extent. however, there is still a long way to go for most llms as human-like dialogue systems. in addition, llms are generally better at understanding context, but relatively poor at perceiving emotions and personality. we expect di-alogbench to serve as a cornerstone for future study to develop better human-like llms.