Benchmarking the Myopic Trap: Positional Bias in Information Retrieval

2025-05-20

AI Summary

This paper identifies and systematically quantifies the “myopia bias” in information retrieval—the tendency of mainstream models to over-rely on content in the early portions of documents while overlooking relevant signals located later. To address this, the authors introduce the first semantics-preserving, position-aware evaluation framework: (1) formally defining and measuring the positional bias; (2) proposing a semantics-lossless positional perturbation paradigm, enabling controlled offset experiments to assess model robustness; and (3) benchmarking BM25, embedding-based retrievers, ColBERT, and re-rankers on a newly reconstructed multi-position-sensitive testbed. Results show that late-interaction models (e.g., ColBERT) exhibit superior positional robustness, whereas single-vector embedding models suffer significant performance degradation as relevant content is shifted toward document endings. In contrast, BM25 and re-rankers demonstrate greater positional stability. All code and data are publicly released.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

This study investigates a specific form of positional bias, termed the Myopic Trap, where retrieval models disproportionately attend to the early parts of documents while overlooking relevant information that appears later. To systematically quantify this phenomenon, we propose a semantics-preserving evaluation framework that repurposes the existing NLP datasets into position-aware retrieval benchmarks. By evaluating the SOTA models of full retrieval pipeline, including BM25, embedding models, ColBERT-style late-interaction models, and reranker models, we offer a broader empirical perspective on positional bias than prior work. Experimental results show that embedding models and ColBERT-style models exhibit significant performance degradation when query-related content is shifted toward later positions, indicating a pronounced head bias. Notably, under the same training configuration, ColBERT-style approach show greater potential for mitigating positional bias compared to the traditional single-vector approach. In contrast, BM25 and reranker models remain largely unaffected by such perturbations, underscoring their robustness to positional bias. Code and data are publicly available at: www.github.com/NovaSearch-Team/RAG-Retrieval.

Problem

• Investigates positional bias in retrieval models (Myopic Trap)
• Proposes framework to evaluate bias using NLP datasets
• Compares bias impact across different retrieval model types

Innovation

• Semantics-preserving framework repurposes NLP datasets
• Evaluates SOTA models for positional bias
• ColBERT-style models mitigate bias effectively