The research paper presents Multi-expert Prompting, a novel method for improving the reliability, safety, and usefulness of Large Language Models (LLMs). Multi-expert Prompting simulates multiple experts within an LLM, collecting their answers to an instruction and aggregating them into a final response. This process leverages the Nominal Group Technique, a human-designed decision-making framework, to ensure a balanced and comprehensive output, surpassing the limitations of single-expert approaches. The authors demonstrate the method’s effectiveness through thorough evaluation on various benchmarks, highlighting its significant improvements in truthfulness, factuality, toxicity reduction, and overall informativeness compared to existing baselines.
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