title: Do RAG Systems Suffer From Positional Bias?
summary: Retrieval Augmented Generation enhances LLM accuracy by adding passages
  retrieved from an external corpus to the LLM prompt. This paper investigates
  how positional bias - the tendency of LLMs to weight information differently
  based on its position in the prompt - affects not only the LLM's capability to
  capitalize on relevant passages, but also its susceptibility to distracting
  passages. Through extensive experiments on three benchmarks, we show how
  state-of-the-art retrieval pipelines, while attempting to retrieve relevant
  passages, systematically bring highly distracting ones to the top ranks, with
  over 60% of queries containing at least one highly distracting passage among
  the top-10 retrieved passages. As a result, the impact of the LLM positional
  bias, which in controlled settings is often reported as very prominent by
  related works, is actually marginal in real scenarios since both relevant and
  distracting passages are, in turn, penalized. Indeed, our findings reveal that
  sophisticated strategies that attempt to rearrange the passages based on LLM
  positional preferences do not perform better than random shuffling.
paper_authors._id: 6836c1815b96c1925376ecce
paper_authors.name: Florin Cuconasu
paper_authors.status: claimed_verified
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paper_authors.fullname: Florin Cuconasu
paper_authors.type: user
paper_authors.user: florin-hf
paper_authors._id: 6836c1815b96c1925376eccf
paper_authors.name: Simone Filice
paper_authors.status: admin_assigned
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paper_authors.fullname: Simone Filice
paper_authors.type: user
paper_authors.user: filice
paper_authors._id: 6836c1815b96c1925376ecd0
paper_authors.name: Guy Horowitz
paper_authors.status: admin_assigned
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paper_authors._id: 63062839df993a789e649e00
paper_authors.avatarUrl: /avatars/48a57ab7458a0c525da76b289a36a9f6.svg
paper_authors.fullname: Guy Horowitz
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paper_authors.user: guyhoro95
paper_authors._id: 6836c1815b96c1925376ecd1
paper_authors.name: Yoelle Maarek
paper_authors.status: admin_assigned
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paper_authors._id: 677fa709d34d476c057cd4cb
paper_authors.avatarUrl: /avatars/54fe4086b5f1e7af7f573d971f01a2c2.svg
paper_authors.fullname: Yoelle Maarek
paper_authors.type: user
paper_authors.user: yoelle
paper_authors._id: 6836c1815b96c1925376ecd2
paper_authors.name: Fabrizio Silvestri
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:25:54.460Z
paper_authors._id: 6075d282134c000d1ae10d54
paper_authors.avatarUrl: /avatars/8691622e1f6325bc378b2268c119b9a0.svg
paper_authors.fullname: Fabrizio Silvestri
paper_authors.type: user
paper_authors.user: fabrizio-silvestri
paper_title: Do RAG Systems Suffer From Positional Bias?
paper_summary: Retrieval Augmented Generation enhances LLM accuracy by adding passages
  retrieved from an external corpus to the LLM prompt. This paper investigates
  how positional bias - the tendency of LLMs to weight information differently
  based on its position in the prompt - affects not only the LLM's capability to
  capitalize on relevant passages, but also its susceptibility to distracting
  passages. Through extensive experiments on three benchmarks, we show how
  state-of-the-art retrieval pipelines, while attempting to retrieve relevant
  passages, systematically bring highly distracting ones to the top ranks, with
  over 60% of queries containing at least one highly distracting passage among
  the top-10 retrieved passages. As a result, the impact of the LLM positional
  bias, which in controlled settings is often reported as very prominent by
  related works, is actually marginal in real scenarios since both relevant and
  distracting passages are, in turn, penalized. Indeed, our findings reveal that
  sophisticated strategies that attempt to rearrange the passages based on LLM
  positional preferences do not perform better than random shuffling.
paper_ai_summary: Retrieval Augmented Generation suffers from high distraction from top-ranked passages, rendering LLM positional bias less impactful than previously thought.
paper_ai_keywords: Retrieval Augmented Generation LLM positional bias relevant passages distracting passages retrieval pipelines