title: Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents
summary: 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 Causal Diagnosis and Correction (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://github.com/WhyDwelledOnAi/Perplexity-Trap.
paper_authors._id: 67d170402850ffdf6f83e2be
paper_authors.name: Haoyu Wang
paper_authors._id: 67d170402850ffdf6f83e2bf
paper_authors.name: Sunhao Dai
paper_authors._id: 67d170402850ffdf6f83e2c0
paper_authors.name: Haiyuan Zhao
paper_authors._id: 67d170402850ffdf6f83e2c1
paper_authors.name: Liang Pang
paper_authors._id: 67d170402850ffdf6f83e2c2
paper_authors.name: Xiao Zhang
paper_authors._id: 67d170402850ffdf6f83e2c3
paper_authors.name: Gang Wang
paper_authors._id: 67d170402850ffdf6f83e2c4
paper_authors.name: Zhenhua Dong
paper_authors._id: 67d170402850ffdf6f83e2c5
paper_authors.name: Jun Xu
paper_authors._id: 67d170402850ffdf6f83e2c6
paper_authors.name: Ji-Rong Wen
paper_title: Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents
paper_summary: 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 Causal Diagnosis and Correction (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://github.com/WhyDwelledOnAi/Perplexity-Trap.
paper_ai_keywords: PLM-based retrieval models LLM-generated content relevance scores source bias causal graph perplexity features retrieval task gradients of the loss functions language modeling task Causal Diagnosis and Correction (CDC)