Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents
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.
67d170402850ffdf6f83e2be
Haoyu Wang
67d170402850ffdf6f83e2bf
Sunhao Dai
67d170402850ffdf6f83e2c0
Haiyuan Zhao
67d170402850ffdf6f83e2c1
Liang Pang
67d170402850ffdf6f83e2c2
Xiao Zhang
67d170402850ffdf6f83e2c3
Gang Wang
67d170402850ffdf6f83e2c4
Zhenhua Dong
67d170402850ffdf6f83e2c5
Jun Xu
67d170402850ffdf6f83e2c6
Ji-Rong Wen
Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents
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.
A causal graph highlights that PLM-based retrievers learn perplexity features leading to source bias, and a method called Causal Diagnosis and Correction (CDC) effectively mitigates this bias.
PLM-based retrieval models LLM-generated content source bias causal graph perplexity features relevance estimation language modeling task retrieval task Causal Diagnosis and Correction (CDC) bias effect explanatory framework