title: Multi-expert Prompting Improves Reliability, Safety, and Usefulness of
    Large Language Models
summary: We present Multi-expert Prompting, a novel enhancement of ExpertPrompting (Xu
  et al., 2023), designed to improve the large language model (LLM) generation.
  Specifically, it guides an LLM to fulfill an input instruction by simulating
  multiple experts, aggregating their responses, and selecting the best among
  individual and aggregated responses. This process is performed in a single
  chain of thoughts through our seven carefully designed subtasks derived from
  the Nominal Group Technique (Ven and Delbecq, 1974), a well-established
  decision-making framework. Our evaluations demonstrate that Multi-expert
  Prompting significantly outperforms ExpertPrompting and comparable baselines in
  enhancing the truthfulness, factuality, informativeness, and usefulness of
  responses while reducing toxicity and hurtfulness. It further achieves
  state-of-the-art truthfulness by outperforming the best baseline by 8.69% with
  ChatGPT. Multi-expert Prompting is efficient, explainable, and highly adaptable
  to diverse scenarios, eliminating the need for manual prompt construction.
paper_authors._id: 67284e2f24e97c8a0709304b
paper_authors.name: Do Xuan Long
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-05T16:09:22.404Z
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paper_authors.avatarUrl: /avatars/411c4ffc2a3e89047a23c6e7442f6ed5.svg
paper_authors.fullname: Do Xuan Long
paper_authors.type: user
paper_authors.user: dxlong2000
paper_authors._id: 67284e2f24e97c8a0709304c
paper_authors.name: Duong Ngoc Yen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-05T16:47:43.606Z
paper_authors._id: 64807cb87d65d9ac173045ad
paper_authors.avatarUrl: /avatars/9f65a57b440e9772f1de5fd6fd425db0.svg
paper_authors.fullname: Duong Ngoc Yen
paper_authors.type: user
paper_authors.user: duongngocyen
paper_authors._id: 67284e2f24e97c8a0709304d
paper_authors.name: Anh Tuan Luu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-05T16:47:49.354Z
paper_authors._id: 655722e80438e0854fae7554
paper_authors.avatarUrl: /avatars/b93a74f7c7880f9fe0f3ffb47e2aef5e.svg
paper_authors.fullname: Luu Anh Tuan
paper_authors.type: user
paper_authors.user: anhtuanluu36
paper_authors._id: 67284e2f24e97c8a0709304e
paper_authors.name: Kenji Kawaguchi
paper_authors._id: 67284e2f24e97c8a0709304f
paper_authors.name: Min-Yen Kan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-05T16:48:01.861Z
paper_authors._id: 628f137053028c04a3b750e4
paper_authors.avatarUrl: /avatars/85d174cc041ab91e724bb9d9d4e46c88.svg
paper_authors.fullname: Min-Yen Kan
paper_authors.type: user
paper_authors.user: knmnyn
paper_authors._id: 67284e2f24e97c8a07093050
paper_authors.name: Nancy F. Chen
paper_title: Multi-expert Prompting Improves Reliability, Safety, and Usefulness of
    Large Language Models
paper_summary: We present Multi-expert Prompting, a novel enhancement of ExpertPrompting (Xu
  et al., 2023), designed to improve the large language model (LLM) generation.
  Specifically, it guides an LLM to fulfill an input instruction by simulating
  multiple experts, aggregating their responses, and selecting the best among
  individual and aggregated responses. This process is performed in a single
  chain of thoughts through our seven carefully designed subtasks derived from
  the Nominal Group Technique (Ven and Delbecq, 1974), a well-established
  decision-making framework. Our evaluations demonstrate that Multi-expert
  Prompting significantly outperforms ExpertPrompting and comparable baselines in
  enhancing the truthfulness, factuality, informativeness, and usefulness of
  responses while reducing toxicity and hurtfulness. It further achieves
  state-of-the-art truthfulness by outperforming the best baseline by 8.69% with
  ChatGPT. Multi-expert Prompting is efficient, explainable, and highly adaptable
  to diverse scenarios, eliminating the need for manual prompt construction.
paper_ai_summary: Multi-expert Prompting enhances LLMs by simulating multiple experts to improve response quality and reduce toxicity, achieving state-of-the-art results in truthfulness.
paper_ai_keywords: Large language models LLM Multi-expert Prompting ExpertPrompting Nominal Group Technique decision-making framework chain of thoughts response quality truthfulness factuality informativeness usefulness toxicity hurtfulness ChatGPT