title: DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate
    Hallucinations
summary: Large Language Models (LLMs) often hallucinate, producing unfaithful or
  factually incorrect outputs by misrepresenting the provided context or
  incorrectly recalling internal knowledge. Recent studies have identified
  specific attention heads within the Transformer architecture, known as
  retrieval heads, responsible for extracting relevant contextual information. We
  hypothesise that masking these retrieval heads can induce hallucinations and
  that contrasting the outputs of the base LLM and the masked LLM can reduce
  hallucinations. To this end, we propose Decoding by Contrasting Retrieval Heads
  (DeCoRe), a novel training-free decoding strategy that amplifies information
  found in the context and model parameters. DeCoRe mitigates potentially
  hallucinated responses by dynamically contrasting the outputs of the base LLM
  and the masked LLM, using conditional entropy as a guide. Our extensive
  experiments confirm that DeCoRe significantly improves performance on tasks
  requiring high contextual faithfulness, such as summarisation (XSum by 18.6%),
  instruction following (MemoTrap by 10.9%), and open-book question answering
  (NQ-Open by 2.4% and NQ-Swap by 5.5%).
paper_authors._id: 671bbe62b91f5a4a244c9c10
paper_authors.name: Aryo Pradipta Gema
paper_authors.status: claimed_verified
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paper_authors.fullname: Aryo Pradipta Gema
paper_authors.type: user
paper_authors.user: aryopg
paper_authors._id: 671bbe62b91f5a4a244c9c11
paper_authors.name: Chen Jin
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paper_authors.fullname: Chen Jin
paper_authors.type: user
paper_authors.user: lxasqjc
paper_authors._id: 671bbe62b91f5a4a244c9c12
paper_authors.name: Ahmed Abdulaal
paper_authors._id: 671bbe62b91f5a4a244c9c13
paper_authors.name: Tom Diethe
paper_authors._id: 671bbe62b91f5a4a244c9c14
paper_authors.name: Philip Teare
paper_authors._id: 671bbe62b91f5a4a244c9c15
paper_authors.name: Beatrice Alex
paper_authors._id: 671bbe62b91f5a4a244c9c16
paper_authors.name: Pasquale Minervini
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paper_authors.fullname: Pasquale Minervini
paper_authors.type: user
paper_authors.user: pminervini
paper_authors._id: 671bbe62b91f5a4a244c9c17
paper_authors.name: Amrutha Saseendran
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-10-25T15:50:59.114Z
paper_authors._id: 6535b54d1922c31a9b1f9b2d
paper_authors.avatarUrl: /avatars/9e0be3f4a844ede08f16155b391e5fd3.svg
paper_authors.fullname: Amrutha Saseendran
paper_authors.type: user
paper_authors.user: saseam
paper_title: DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate
    Hallucinations
paper_summary: Large Language Models (LLMs) often hallucinate, producing unfaithful or
  factually incorrect outputs by misrepresenting the provided context or
  incorrectly recalling internal knowledge. Recent studies have identified
  specific attention heads within the Transformer architecture, known as
  retrieval heads, responsible for extracting relevant contextual information. We
  hypothesise that masking these retrieval heads can induce hallucinations and
  that contrasting the outputs of the base LLM and the masked LLM can reduce
  hallucinations. To this end, we propose Decoding by Contrasting Retrieval Heads
  (DeCoRe), a novel training-free decoding strategy that amplifies information
  found in the context and model parameters. DeCoRe mitigates potentially
  hallucinated responses by dynamically contrasting the outputs of the base LLM
  and the masked LLM, using conditional entropy as a guide. Our extensive
  experiments confirm that DeCoRe significantly improves performance on tasks
  requiring high contextual faithfulness, such as summarisation (XSum by 18.6%),
  instruction following (MemoTrap by 10.9%), and open-book question answering
  (NQ-Open by 2.4% and NQ-Swap by 5.5%).
paper_ai_summary: A training-free decoding strategy called DeCoRe uses contrastive outputs from masked and unmasked retrieval heads in Transformer architectures to reduce hallucinations in Large Language Models (LLMs).
paper_ai_keywords: Large Language Models (LLMs) hallucination attention heads retrieval heads DeCoRe conditional entropy summarisation instruction following open-book question answering