title: The Distracting Effect: Understanding Irrelevant Passages in RAG
summary: A well-known issue with Retrieval Augmented Generation (RAG) is that
  retrieved passages that are irrelevant to the query sometimes distract the
  answer-generating LLM, causing it to provide an incorrect response. In this
  paper, we shed light on this core issue and formulate the distracting effect of
  a passage w.r.t. a query (and an LLM). We provide a quantifiable measure of the
  distracting effect of a passage and demonstrate its robustness across LLMs.
    Our research introduces novel methods for identifying and using hard
  distracting passages to improve RAG systems. By fine-tuning LLMs with these
  carefully selected distracting passages, we achieve up to a 7.5% increase in
  answering accuracy compared to counterparts fine-tuned on conventional RAG
  datasets. Our contribution is two-fold: first, we move beyond the simple binary
  classification of irrelevant passages as either completely unrelated vs.
  distracting, and second, we develop and analyze multiple methods for finding
  hard distracting passages. To our knowledge, no other research has provided
  such a comprehensive framework for identifying and utilizing hard distracting
  passages.
paper_authors._id: 682acf157ee0ab9140c94cb8
paper_authors.name: Chen Amiraz
paper_authors._id: 682acf157ee0ab9140c94cb9
paper_authors.name: Florin Cuconasu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T15:19:03.169Z
paper_authors._id: 62bfff6788fdef8ecde8c45b
paper_authors.avatarUrl: /avatars/637da88b8dce01bdc2a4c1319cc882e3.svg
paper_authors.fullname: Florin Cuconasu
paper_authors.type: user
paper_authors.user: florin-hf
paper_authors._id: 682acf157ee0ab9140c94cba
paper_authors.name: Simone Filice
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-19T07:32:13.228Z
paper_authors._id: 652028f9974423bd3ea33ae2
paper_authors.avatarUrl: /avatars/50b749f9a95a3b0fb7a2bfcf17c34295.svg
paper_authors.fullname: Simone Filice
paper_authors.type: user
paper_authors.user: filice
paper_authors._id: 682acf157ee0ab9140c94cbb
paper_authors.name: Zohar Karnin
paper_title: The Distracting Effect: Understanding Irrelevant Passages in RAG
paper_summary: A well-known issue with Retrieval Augmented Generation (RAG) is that
  retrieved passages that are irrelevant to the query sometimes distract the
  answer-generating LLM, causing it to provide an incorrect response. In this
  paper, we shed light on this core issue and formulate the distracting effect of
  a passage w.r.t. a query (and an LLM). We provide a quantifiable measure of the
  distracting effect of a passage and demonstrate its robustness across LLMs.
    Our research introduces novel methods for identifying and using hard
  distracting passages to improve RAG systems. By fine-tuning LLMs with these
  carefully selected distracting passages, we achieve up to a 7.5% increase in
  answering accuracy compared to counterparts fine-tuned on conventional RAG
  datasets. Our contribution is two-fold: first, we move beyond the simple binary
  classification of irrelevant passages as either completely unrelated vs.
  distracting, and second, we develop and analyze multiple methods for finding
  hard distracting passages. To our knowledge, no other research has provided
  such a comprehensive framework for identifying and utilizing hard distracting
  passages.
paper_ai_summary: Methods for identifying and utilizing hard distracting passages in Retrieval Augmented Generation (RAG) systems improve answer-generating LLMs by up to 7.5% in accuracy.
paper_ai_keywords: Retrieval Augmented Generation RAG answer-generating LLM distracting effect fine-tuning conventional RAG datasets binary classification hard distracting passages