LLM s as Narcissistic Evaluators: When Ego Inflates Evaluation Scores (2024)

Abstract: Automatic evaluation of generated textual content presents an ongoing challenge within the field of NLP. Given the impressive capabilities of modern language models (LMs) across diverse NLP tasks, there is a growing trend to employ these models in creating innovative evaluation metrics for automated assessment of generation tasks. This paper investigates a pivotal question: Do language model-driven evaluation metrics inherently exhibit bias favoring texts generated by the same underlying language model? Specifically, we assess whether prominent LM-based evaluation metrics (e.g. BARTScore, T5Score, and GPTScore) demonstrate a favorable bias toward their respective underlying LMs in the context of summarization tasks. Our findings unveil a latent bias, particularly pronounced when such evaluation metrics are used in a reference-free manner without leveraging gold summaries. These results underscore that assessments provided by generative evaluation models can be influenced by factors beyond the inherent text quality, highlighting the necessity of developing more reliable evaluation protocols in the future.

## introduction evaluation is a fundamental element in both tracking progress and ensuring meaningful advancements across various dimensions within the field of natural language processing. therefore, the reliability of evaluation metrics plays a critical role in this process. evaluating generated texts is one of the challenging and open problems in nlp given that different forms can convey the same meaning. this challenge has led to the development of various evaluation metrics for tasks involving natural language generation (nlg). while human evaluation by experts stands as the most reliable approach for assessing generated outputs, it is costly and time-consuming, limiting its broader use. as a result, automatic evaluation metrics have emerged ## related work reference-based evaluation metrics reference-based metrics are commonly used to evaluate text generation tasks, including summarization, by measuring the similarity between generated and reference texts. traditionally, metrics like bleu @xcite and rouge @xcite were employed to assess a generated text based on surface-level similarities, measured through the n-gram overlap between the generated and reference texts. recent trends in summarization evaluation lean towards semantic-level assessments, moving beyond direct word overlap comparisons. notable metrics embracing this approach include bertscore @xcite , moverscore @xcite , bartscore @xcite , bleurt @xcite , and variations thereof. by leveraging pretrained language models, these metrics focus on capturing semantic content, providing a more nuanced and accurate evaluation of summarization system outputs. ## methodology to investigate the impact of the model's self-biasdetermining whether a language model-based evaluator favours outputs generated by a similar language model-we conduct a comprehensive series of experiments involving both quantitative comparisons and qualitative analysis. our quantitative comparisons involve using language models of varying sizes and finetuning configurations as both the evaluator and generator models. this structured approach enables us to systematically examine the potential bias across different lm configurations. subsequently, we verify the results through qualitative analysis using a subset of models' summaries that are accompanied by human evaluation to fur-ther demonstrate that higher scores produced by evaluators as a result of self-bias do not necessarily correlate with higher quality generated outputs. ## conclusions based on experiments, we make the following conclusions: first, the popularity of generative evaluation metrics, such as bartscore, is on the rise for evaluating the factual accuracy of generated content-a critical concern in modern generator models. however, our results reveal that this evaluation