LLMs may Dominate Information Access: Neural Retrievers are Biased Towards LLM-Generated Texts

• URL: http://arxiv.org/abs/2310.20501v2
• Date: Sun, 14 Jan 2024 14:41:06 GMT
• Title: LLMs may Dominate Information Access: Neural Retrievers are Biased Towards LLM-Generated Texts
• Authors: Sunhao Dai, Yuqi Zhou, Liang Pang, Weihao Liu, Xiaolin Hu, Yong Liu, Xiao Zhang, Gang Wang and Jun Xu
• Abstract summary: Large language models (LLMs) have revolutionized the paradigm of information retrieval (IR) applications. Surprisingly, our findings indicate that neural retrieval models tend to rank LLM-generated documents higher. To mitigate the source bias, we also propose a plug-and-play debiased constraint for the optimization objective.
• Score: 36.73455759259717
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recently, the emergence of large language models (LLMs) has revolutionized the paradigm of information retrieval (IR) applications, especially in web search. With their remarkable capabilities in generating human-like texts, LLMs have created enormous texts on the Internet. As a result, IR systems in the LLMs era are facing a new challenge: the indexed documents now are not only written by human beings but also automatically generated by the LLMs. How these LLM-generated documents influence the IR systems is a pressing and still unexplored question. In this work, we conduct a quantitative evaluation of different IR models in scenarios where both human-written and LLM-generated texts are involved. Surprisingly, our findings indicate that neural retrieval models tend to rank LLM-generated documents higher. We refer to this category of biases in neural retrieval models towards the LLM-generated text as the \textbf{source bias}. Moreover, we discover that this bias is not confined to the first-stage neural retrievers, but extends to the second-stage neural re-rankers. Then, we provide an in-depth analysis from the perspective of text compression and observe that neural models can better understand the semantic information of LLM-generated text, which is further substantiated by our theoretical analysis. To mitigate the source bias, we also propose a plug-and-play debiased constraint for the optimization objective, and experimental results show the effectiveness. Finally, we discuss the potential severe concerns stemming from the observed source bias and hope our findings can serve as a critical wake-up call to the IR community and beyond. To facilitate future explorations of IR in the LLM era, the constructed two new benchmarks and codes will later be available at \url{https://github.com/KID-22/LLM4IR-Bias}.

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