Multi-expert Prompting Improves Reliability, Safety, and Usefulness of
  Large Language Models
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.
67284e2f24e97c8a0709304b
Do Xuan Long
claimed_verified
2024-11-05T16:09:22.404Z
63a9a0d13453852ef53c0b37
/avatars/411c4ffc2a3e89047a23c6e7442f6ed5.svg
Do Xuan Long
user
dxlong2000
67284e2f24e97c8a0709304c
Duong Ngoc Yen
admin_assigned
2024-11-05T16:47:43.606Z
64807cb87d65d9ac173045ad
/avatars/9f65a57b440e9772f1de5fd6fd425db0.svg
Duong Ngoc Yen
user
duongngocyen
67284e2f24e97c8a0709304d
Anh Tuan Luu
admin_assigned
2024-11-05T16:47:49.354Z
655722e80438e0854fae7554
/avatars/b93a74f7c7880f9fe0f3ffb47e2aef5e.svg
Luu Anh Tuan
user
anhtuanluu36
67284e2f24e97c8a0709304e
Kenji Kawaguchi
67284e2f24e97c8a0709304f
Min-Yen Kan
admin_assigned
2024-11-05T16:48:01.861Z
628f137053028c04a3b750e4
/avatars/85d174cc041ab91e724bb9d9d4e46c88.svg
Min-Yen Kan
user
knmnyn
67284e2f24e97c8a07093050
Nancy F. Chen
Multi-expert Prompting Improves Reliability, Safety, and Usefulness of
  Large Language Models
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.
Multi-expert Prompting enhances LLMs by simulating multiple experts to improve response quality and reduce toxicity, achieving state-of-the-art results in truthfulness.
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