DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate
  Hallucinations
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%).
671bbe62b91f5a4a244c9c10
Aryo Pradipta Gema
claimed_verified
2024-10-28T10:37:19.549Z
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Aryo Pradipta Gema
user
aryopg
671bbe62b91f5a4a244c9c11
Chen Jin
claimed_verified
2025-02-13T08:26:51.059Z
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Chen Jin
user
lxasqjc
671bbe62b91f5a4a244c9c12
Ahmed Abdulaal
671bbe62b91f5a4a244c9c13
Tom Diethe
671bbe62b91f5a4a244c9c14
Philip Teare
671bbe62b91f5a4a244c9c15
Beatrice Alex
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Pasquale Minervini
claimed_verified
2024-10-28T10:37:16.706Z
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Pasquale Minervini
user
pminervini
671bbe62b91f5a4a244c9c17
Amrutha Saseendran
extracted_pending
2024-10-25T15:50:59.114Z
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Amrutha Saseendran
user
saseam
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate
  Hallucinations
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%).
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).
Large Language Models (LLMs) hallucination attention heads retrieval heads DeCoRe conditional entropy summarisation instruction following open-book question answering