Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization (2023)

Abstract: With the recent undeniable advancement in reasoning abilities in large language models (LLMs) like ChatGPT and GPT-4, there is a growing trend for using LLMs on various tasks. One area where LLMs can be employed is as an alternative evaluation metric for complex generative tasks, which generally demands expensive human judges to complement the traditional automatic metrics for various evaluation dimensions such as fluency and consistency. In this work, we conduct extensive analysis to investigate the stability and reliability of LLMs as automatic evaluators for abstractive summarization. We found that while ChatGPT and GPT-4 outperform the commonly used automatic metrics, they are not ready as human replacements due to significant limitations. That is, LLM evaluators rate each candidate system inconsistently and are dimension-dependent. They also struggle to compare candidates with close performance and become more unreliable with higher-quality summaries by obtaining a lower correlation with humans. In other words, with better abstractive summarization systems being introduced at a fast pace, LLMs may result in misleading and unreliable evaluations.

## introduction the desire for inexpensive and fast automatic metrics has never stopped growing. in certain tasks like extractive summarization, where full source sentences are selected to appear in the summaries, simple n-gram overlap metrics against the "gold" summaries like rouge @xcite or bleu @xcite may work well because the correct answer space is narrow. however, for more open tasks like abstractive summarization, there are countless equally good summaries and the "gold" summaries become less important. although many neural-based metrics such as bertscore and bartscore @xcite , are advocated as more human-aligned, the evaluation criteria are also becoming increasingly complex. as a result, abstractive summarization may not be sufficiently evaluated with automatic metrics @xcite , and often require extensive human evaluations as complements @xcite . however, human evaluations often come with hefty costs and slow iteration cycles, while also being difficult to reproduce and standardize due to small sample sizes and potential human biases @xcite . recent large language models (llms) like chat-gpt and gpt-4 (openai, 2023) have demonstrated outstanding capabilities in language comprehension and reasoning. this leads to a growing trend of employing llms as evaluators for complex language generation tasks by prompting them with carefully and elaborately crafted instructions @xcite @xcite @xcite . despite the preliminary success suggested by such works, it is still inconclusive as to what degree of confidence we can trust the evaluation results produced by llms across different dimensions, despite their supposedly high average correlation with humans. it is also unclear if certain llm-based metrics are more reliable than others, or if their reliability and fairness vary for different candidate systems. in this work, we conduct extensive analysis to assess whether llm evaluators can reliably replace human judges. specifically, we incorporate two common human evaluation approaches with llm evaluators, namely likert-scale scoring @xcite @xcite and head-to-head (h2h) comparisons @xcite @xcite . for likert-scale scoring, we explore direct reason-thenscore (rts) generation and a multiple-choice question (mcq) method. the former instructs the llm to provide reasoning before giving a score, while the latter simply prompts it to choose a specific score with a pre-determined description as the reason. for the head-to-head (h2h) comparison, we prompt llm for a preference over the summaries from two compared candidate systems. our experiments show that llm evaluators, with rts and mcq, outperform existing automatic metrics @xcite . however, they are not ready to be reliable alternatives for human evaluation yet. specifically, (i) llm evaluators struggle to distinguish candidates with close performances ( § 4.2.1). (ii) llm evaluators are candidate-dependent, meaning they do not exhibit highly consistent degrees of human alignment for different candidates ( § 4.2.3). thus, they may unfairly favor or disfavor an evaluated candidate. (iii) llm evaluators are dimensiondependent, meaning they have varying degrees of evaluation capabilities for different dimensions like coherence and fluency ( § 4.2.3). (iv) lastly, as the quality of summaries improves with better candidates, llm evaluators become unreliably less correlated with human judgments, according to our newly proposed meta-correlation metric ( § 4.2.4). while we still call for a better automatic metric, in the meantime, we suggest a temporary solution in § 5 for abstractive summarization practitioners to use llms more reliably. specifically, we advocate calculating the correlation between rts and mcq as a preliminary indicator of the reliability of the llm for certain dimensions. if rts and mcq do not generally agree with each other, then further human evaluations are required. human evaluation human evaluation can be conducted with different approaches. some work @xcite @xcite @xcite @xcite employ a likert scale to evaluate the summaries on discrete ranges, such as from 1 to 5. meanwhile, many others suggest comparison approaches by asking human annotators to select the best summary out of 2 or more generated summaries from different systems @xcite @xcite @xcite . following this, we test llm-based evaluators using both approaches with human-friendly instruction prompts. automatic evaluation rouge @xcite has been a common lexical overlap metric to evaluate summarization systems. apparently, rouge is not sufficient for abstractive summarization, because the "gold" labels it relies on cannot comprehensively account for the complexity and variability of this task. in addition, the common usage of sentence fusion techniques and novel words for abstractive summarization may make rouge even less reliable. @xcite propose the neural-based bertscore, which leverages the bert word embeddings to compute the semantic similarity among tokens. @xcite later introduce bartscore, which uses bart @xcite to compute the probability of a summary given its input article. nonetheless, these metrics may not reflect all of the complicated evaluation dimensions required for abstractive summarization mentioned earlier, nor do they have sufficiently high correlations with humans. ## llm as a zero-shot evaluator we investigate an llm's evaluation capabilities in the dimensions of coherence, consistency, fluency, and relevance respectively, as defined by @xcite (see appendix a). following common human evaluation approaches, we propose two methods for likert-scale scoring, namely the reason-then-score method and the multiple-choice question method, as well as one method for headto-head comparisons. we describe each method in § 3.1 using the relevance dimension as an example (see more prompts and details in appendix b). we further experiment with alternative phrasings for different methods in appendix c. besides exploring different evaluation methods, the stability of llm-based evaluations across different summarization systems is equally important. ideally, a stable llm evaluator should perform equally well regardless of the evaluated systems, with a close (if not identical) degree of alignment with human judgments. in § 3.2, we propose a meta-correlation metric and explain how it can gauge the extent to which llm evaluators' performances depend on the evaluated systems, which indicates how stable and reliable they may be with evaluating any future candidate systems. ## a temporary efficient framework despite the aforementioned limitations, it may be hard to resist the temptation of using llm evaluators given their superiority over other automatic metrics. in such a case, one should be able to tell when llm evaluators are more likely to be unreliable and employ further human evaluation when necessary. to this end, we suggest combining the rts and mcq scores as a cost-efficient framework. specifically, we calculate the correlation between rts and mcq scores for the i th candidate system as a reliability indicator: then, we can loosely infer that up to a reliability tolerance @xmath0, 1), the llm evaluators (either rts or mcq) are reliable if r @xmath1. in other words, given a candidate i, if rts and mcq agree with each other up to a certain degree of tolerance r, we may assume the evaluator is reliable enough to avoid invoking further human evaluation. to validate this theory, we measure the correlations ρ(r i , p rts i ) or ρ(r i , p mcq i ), where p rt s/m cq i is the performance of either method as defined in equation ( 2 ). given significantly large positive values of either ρ(r i , p rts i ) or ρ(r i , p mcq i ), we can then conclude that r i can be used as a reliable indicator for the performance of the corresponding method. ## conclusion we explore the potential of using llms with different prompting techniques as metrics for abstractive summarization systems. our extensive analysis suggests that while llms like chatgpt perform better than commonly used automatic metrics across different summarization systems and dimensions, they are still not ready to replace human evaluators because they are candidate-and dimension-dependent, and they do not align well with human when comparing high-quality candidates. nonetheless, if an llm evaluator is to be used, we suggest combining multiple evaluation methods as a preliminary indicator to determine whether the metric is likely to be unreliable and whether further human evaluation is required.