title: Retrieval-Augmented Generation with Conflicting Evidence
summary: Large language model (LLM) agents are increasingly employing
  retrieval-augmented generation (RAG) to improve the factuality of their
  responses. However, in practice, these systems often need to handle ambiguous
  user queries and potentially conflicting information from multiple sources
  while also suppressing inaccurate information from noisy or irrelevant
  documents. Prior work has generally studied and addressed these challenges in
  isolation, considering only one aspect at a time, such as handling ambiguity or
  robustness to noise and misinformation. We instead consider multiple factors
  simultaneously, proposing (i) RAMDocs (Retrieval with Ambiguity and
  Misinformation in Documents), a new dataset that simulates complex and
  realistic scenarios for conflicting evidence for a user query, including
  ambiguity, misinformation, and noise; and (ii) MADAM-RAG, a multi-agent
  approach in which LLM agents debate over the merits of an answer over multiple
  rounds, allowing an aggregator to collate responses corresponding to
  disambiguated entities while discarding misinformation and noise, thereby
  handling diverse sources of conflict jointly. We demonstrate the effectiveness
  of MADAM-RAG using both closed and open-source models on AmbigDocs -- which
  requires presenting all valid answers for ambiguous queries -- improving over
  strong RAG baselines by up to 11.40% and on FaithEval -- which requires
  suppressing misinformation -- where we improve by up to 15.80% (absolute) with
  Llama3.3-70B-Instruct. Furthermore, we find that RAMDocs poses a challenge for
  existing RAG baselines (Llama3.3-70B-Instruct only obtains 32.60 exact match
  score). While MADAM-RAG begins to address these conflicting factors, our
  analysis indicates that a substantial gap remains especially when increasing
  the level of imbalance in supporting evidence and misinformation.
paper_authors._id: 6801c2a379ba651f02e807ba
paper_authors.name: Han Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-18T09:38:57.694Z
paper_authors._id: 617df9bb402d4d8f8eee3737
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1635645820205-noauth.jpeg
paper_authors.fullname: Han Wang
paper_authors.type: user
paper_authors.user: HanNight
paper_authors._id: 6801c2a379ba651f02e807bb
paper_authors.name: Archiki Prasad
paper_authors._id: 6801c2a379ba651f02e807bc
paper_authors.name: Elias Stengel-Eskin
paper_authors._id: 6801c2a379ba651f02e807bd
paper_authors.name: Mohit Bansal
paper_title: Retrieval-Augmented Generation with Conflicting Evidence
paper_summary: Large language model (LLM) agents are increasingly employing
  retrieval-augmented generation (RAG) to improve the factuality of their
  responses. However, in practice, these systems often need to handle ambiguous
  user queries and potentially conflicting information from multiple sources
  while also suppressing inaccurate information from noisy or irrelevant
  documents. Prior work has generally studied and addressed these challenges in
  isolation, considering only one aspect at a time, such as handling ambiguity or
  robustness to noise and misinformation. We instead consider multiple factors
  simultaneously, proposing (i) RAMDocs (Retrieval with Ambiguity and
  Misinformation in Documents), a new dataset that simulates complex and
  realistic scenarios for conflicting evidence for a user query, including
  ambiguity, misinformation, and noise; and (ii) MADAM-RAG, a multi-agent
  approach in which LLM agents debate over the merits of an answer over multiple
  rounds, allowing an aggregator to collate responses corresponding to
  disambiguated entities while discarding misinformation and noise, thereby
  handling diverse sources of conflict jointly. We demonstrate the effectiveness
  of MADAM-RAG using both closed and open-source models on AmbigDocs -- which
  requires presenting all valid answers for ambiguous queries -- improving over
  strong RAG baselines by up to 11.40% and on FaithEval -- which requires
  suppressing misinformation -- where we improve by up to 15.80% (absolute) with
  Llama3.3-70B-Instruct. Furthermore, we find that RAMDocs poses a challenge for
  existing RAG baselines (Llama3.3-70B-Instruct only obtains 32.60 exact match
  score). While MADAM-RAG begins to address these conflicting factors, our
  analysis indicates that a substantial gap remains especially when increasing
  the level of imbalance in supporting evidence and misinformation.
paper_ai_summary: MADAM-RAG, a multi-agent retrieval-augmented generation approach, enhances response factuality by handling ambiguity and misinformation through debate, outperforming existing RAG baselines.
paper_ai_keywords: retrieval-augmented generation RAG RAMDocs MADAM-RAG multi-agent ambiguity misinformation noise disambiguation exact match score AmbigDocs FaithEval Llama3.3-70B-Instruct