Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents
Keywords: Large Language Models, LLM-Generated Content, Infomation Retrieval, Source Bias
TL;DR: We verify the causal effect of document perplexity on estimated relevance scores in PLM-based retrievers to explain the cause of source bias.
Abstract: Previous studies have found that PLM-based retrieval models exhibit a preference for LLM-generated content, assigning higher relevance scores to these documents even when their semantic quality is comparable to human-written ones. This phenomenon, known as source bias, threatens the sustainable development of the information access ecosystem. However, the underlying causes of source bias remain unexplored. In this paper, we explain the process of information retrieval with a causal graph and discover that PLM-based retrievers learn perplexity features for relevance estimation, causing source bias by ranking the documents with low perplexity higher. Theoretical analysis further reveals that the phenomenon stems from the positive correlation between the gradients of the loss functions in language modeling task and retrieval task. Based on the analysis, a causal-inspired inference-time debiasing method is proposed, called $\textbf{C}$ausal $\textbf{D}$iagnosis and $\textbf{C}$orrection (CDC). CDC first diagnoses the bias effect of the perplexity and then separates the bias effect from the overall estimated relevance score. Experimental results across three domains demonstrate the superior debiasing effectiveness of CDC, emphasizing the validity of our proposed explanatory framework. Source codes are available at https://anonymous.4open.science/r/Perplexity-Trap-D6FE.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 1134
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