title: When to Speak, When to Abstain: Contrastive Decoding with Abstention
summary: Large Language Models (LLMs) demonstrate exceptional performance across
  diverse tasks by leveraging both pre-trained knowledge (i.e., parametric
  knowledge) and external knowledge (i.e., contextual knowledge). While
  substantial efforts have been made to leverage both forms of knowledge,
  scenarios in which the model lacks any relevant knowledge remain underexplored.
  Such limitations can result in issues like hallucination, causing reduced
  reliability and potential risks in high-stakes applications. To address such
  limitations, this paper extends the task scope to encompass cases where the
  user's request cannot be fulfilled due to the lack of relevant knowledge. To
  this end, we introduce Contrastive Decoding with Abstention (CDA), a
  training-free decoding method that empowers LLMs to generate responses when
  relevant knowledge is available and to abstain otherwise. CDA evaluates the
  relevance of each knowledge for a given query, adaptively determining which
  knowledge to prioritize or which to completely ignore. Extensive experiments
  with four LLMs on three question-answering datasets demonstrate that CDA can
  effectively perform accurate generation and abstention simultaneously. These
  findings highlight CDA's potential to broaden the applicability of LLMs,
  enhancing reliability and preserving user trust.
paper_authors._id: 6762627c1b66b28261b4bf71
paper_authors.name: Hyuhng Joon Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-18T09:51:18.410Z
paper_authors._id: 6077e727048132eef27894b0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/R49vipboidpLkGra_h1z5.png
paper_authors.fullname: Hyuhng Joon Kim
paper_authors.type: user
paper_authors.user: heyjoonkim
paper_authors._id: 6762627c1b66b28261b4bf72
paper_authors.name: Youna Kim
paper_authors._id: 6762627c1b66b28261b4bf73
paper_authors.name: Sang-goo Lee
paper_authors._id: 6762627c1b66b28261b4bf74
paper_authors.name: Taeuk Kim
paper_title: When to Speak, When to Abstain: Contrastive Decoding with Abstention
paper_summary: Large Language Models (LLMs) demonstrate exceptional performance across
  diverse tasks by leveraging both pre-trained knowledge (i.e., parametric
  knowledge) and external knowledge (i.e., contextual knowledge). While
  substantial efforts have been made to leverage both forms of knowledge,
  scenarios in which the model lacks any relevant knowledge remain underexplored.
  Such limitations can result in issues like hallucination, causing reduced
  reliability and potential risks in high-stakes applications. To address such
  limitations, this paper extends the task scope to encompass cases where the
  user's request cannot be fulfilled due to the lack of relevant knowledge. To
  this end, we introduce Contrastive Decoding with Abstention (CDA), a
  training-free decoding method that empowers LLMs to generate responses when
  relevant knowledge is available and to abstain otherwise. CDA evaluates the
  relevance of each knowledge for a given query, adaptively determining which
  knowledge to prioritize or which to completely ignore. Extensive experiments
  with four LLMs on three question-answering datasets demonstrate that CDA can
  effectively perform accurate generation and abstention simultaneously. These
  findings highlight CDA's potential to broaden the applicability of LLMs,
  enhancing reliability and preserving user trust.
paper_ai_keywords: Contrastive Decoding with Abstention (CDA) pre-trained knowledge external knowledge contextual knowledge hallucination reliability high-stakes applications task scope query feature reliability user trust