A Closer Look into Using Large Language Models for Automatic Evaluation (2023)

Abstract: Using large language models (LLMs) to evaluate text quality has recently gained popularity. Some existing prior works explore the idea of using LLMs for evaluation, while they differ in some details of the evaluation process. In this paper, we analyze *LLM evaluation* and *G-Eval*, and we discuss how those details in the evaluation process change how well the ratings given by LLMs correlate with human ratings. We find that the auto Chain-of-Thought (CoT) used in G-Eval does not always make G-Eval more aligned with human ratings. We also show that forcing the LLM to output only a numeric rating, as in G-Eval, is suboptimal. Last, we reveal that asking the LLM to explain its own ratings consistently improves the correlation between the ChatGPT and human ratings and pushes state-of-the-art (SoTA) correlations on two meta-evaluation datasets.

## introduction large language models (llms) trained with task instructions and human feedback can follow natural language instructions to complete a task @xcite @xcite . recently, the instructionfollowing ability of llms makes them promising candidates for automatic evaluation @xcite @xcite . by simply instructing the llms on how to rate and giving the llms the sample to be rated, the llm can follow the instructions and provide a rating of the sample. @xcite propose llm evaluation and @xcite propose g-eval; both of which use llms to evaluate samples by giving the llm instructions, and they both show that some llms can yield evaluation results that are aligned to the 1 in this paper, the term llm evaluation is used to refer to the specific method proposed by @xcite . given that llm evaluation and g-eval have already received significant attention shortly after publication, these methods will likely revolutionize the evaluation in nlp. therefore, conducting a detailed analysis of these approaches is essential and timely. this paper aims to identify the crucial components in llm evaluation and g-eval that contribute to stronger correlations with human ratings. based on our analysis, we provide guidelines on how to use llms for automatic evaluations. we have the following findings: ## experiment setup our paper studies what components in llm evaluation and g-eval make the ratings generated by llm correlate with human ratings better, and we aim to improve the correlation. ## conclusion we study how to better use chatgpt as an automatic evaluation tool by scrutinizing llm evaluation and g-eval. we provide concrete guidelines and show that by using those guidelines, the correlations of several evaluated attributes given by chatgpt, a publicly usable model, can be higher than or comparable to the ratings given by gpt-4, a highly restricted and pricey model. we also show that the evaluation results based on our guidelines improve the best correlation that chatgpt's rating can achieve. we believe our results and guidelines help future researchers better use llms for evaluation.