LLMs are Biased Evaluators But Not Biased for Retrieval Augmented Generation
Authors: Yen-Shan Chen, Jin Jing, Ying-Jung Tsou, Chao-Wei Huang, Yun-Nung Chen


Reviewer paper summaries: This paper investigates whether LLMs exhibit a preference for LLM-generated (self-preference) text in the RAG scenario. It explores whether such bias exists in GPT-3.5-turbo and LLAMA2-70B-Chat when using the Natural Questions dataset. Specifically, the authors assess whether there is bias when using LLMs as text relevance scorers and which document the models tend to prefer when given both a model-generated document and a human-written document. The experimental results indicate that GPT-3.5-turbo is less susceptible to self-preference influence compared to LLAMA2-70B-Chat.
Summary of strengths: It is meaningful to explore the potential bias from retrieved documents for RAG systems. The retrieved documents may contain text with a special style that more easily influences the LLM, causing the LLM to exhibit undesirable behaviors. Investigating whether the content generated by the model itself may introduce bias into the RAG system is a valuable subtopic.
Summary of weaknesses: 1. The authors claim that there is no significant self-preference effect in RAG frameworks. However, this is not accurate. Experimental results show that GPT-3.5-turbo is less affected by self-preference, but LLama2-70B-Chat exhibits significantly different characteristics from GPT-3.5-turbo, being more susceptible to self-preference effects. Claiming "there is no significant self-preference effect in RAG frameworks" is inaccurate. Additionally, there is a lack of analysis on why LLama2-70B-Chat exhibits different characteristics compared to GPT-3.5.
2. In my opinion, the Pointwise Reranking Phase is not a common stage in RAG frameworks. Typically, RAG does not use LLMs as retrievers to find documents relevant to the query but instead uses BM25 or other lightweight retrieval models due to the large number of candidate documents. Even in scenarios where further filtering of retrieved documents is necessary, Pointwise Reranking is not common. It is better to evaluate the bias under common RAG settings.
3. The writing needs improvement. For example, it is hard to understand how the pairwise score is calculated. Is the score generated by prompting LLM, or is it calculated based on frequency?
Comments, suggestions and typos: It is better to add a number to each equation.
Overall assessment scores: 2