Multi-expert Prompting Improves Reliability, Safety and Usefulness of Large Language Models (2024)

Abstract: 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.

## introduction pre-trained large language models (llms) @xcite @xcite @xcite acquire extensive knowledge during training, demonstrating exceptional abilities as general-purpose problem solvers. as they have made increasing impacts on human life, it is essential to ensure these systems align with human intentions by improving their reliability, safety, and usefulness to meet users' expectations @xcite . among the alignment methods, recent studies @xcite @xcite highlight that llms can mimic expected behaviors of specific agents when cast with sufficient descriptions. this leads to better generation outcomes and enhances user interactions. notably, @xcite introduce expert-prompting directing llms to answer questions as generated experts. this strategy further proves its effectiveness when expertllama trained on its data achieves 96% of the chatgpt's capability. however, is relying on a single expert llm sufficient for diverse user queries? our answer is no. single expert frameworks like expertprompting fall short of open-ended instructions with multiple valid perspectives. for instance, in response to the question "is it ethical to eat meat?" in fig. 1 , expertprompting casts the llm as an ethicist offering a simplistic answer, labeling it as unethical. this approach introduces bias and a dismissive attitude towards other perspectives, such as those of non-vegetarians. ideally, responses to such questions should encompass various other viewpoints addressing multiple dimensions of the issue, such as nutritional and environmental aspects. this highlights that a single expert can introduce biases and limit the depth needed for considering varied perspectives in addressing open-ended instructions. inspired by the above observation, we present a novel and efficient extension of expertprompting named multi-expert prompting, which addresses the need for multiple perspectives. it involves two main steps (fig. 2 ). first, given an input instruction, multi-expert prompting instructs an llm to generate n expert identities with their concise, onesentence role descriptions tailored to the instruction in a zero-shot prompting style. unlike ex-pertprompting @xcite , which relies on generating detailed role descriptions by few-shot hand-crafted demonstrations, our approach does not require demonstrations and is more versatile as detailed descriptions are unnecessary ( §6.1). multiexpert prompting then casts the llm as distinct experts, each responding to the instruction independently. second, it chooses a single best response by aggregating the individual responses and evaluating it together with individual ones through a novel, seven-subtask method in a single chain of thought @xcite following nominal group technique (ngt; @xcite . multi-expert prompting is related to recent efforts in reasoning over multi-agent responses, such as multi-agent debate @xcite and universal self-consistency (usc) @xcite . it distinguishes itself by aggregating expert responses in a single turn without iterative refinement. moreover, its response aggregation is based on the human-designed ngt framework, contrasting with the llm-generated plans in autogen @xcite and autoagents @xcite . finally, it differs from metagpt @xcite by employing diverse domain experts to address questions in parallel, instead of in sequence. multi-expert prompting is the first to tackle the challenge of aggregating multi-agent long-form responses in a single turn based on well-studied perspectives from management sciences. it significantly outperforms baselines in improving the truthfulness, factuality, toxicity, hurtfulness, informativeness, and usefulness of llms by leveraging only three experts, achieving state-of-the-art truthfulness. in addition, it is highly adaptable, explainable, and beneficial for open-ended tasks where diverse expert opinions are valued. ## background we introduce expertprompting @xcite and the nominal group technique (ngt) @xcite , both serving as foundational elements for multi-expert prompting. expertprompting @xcite . expert-prompting is a prompting technique designed to enhance the responses of an llm by leveraging the model's capability to answer as experts. given an input instruction, it begins by prompting the llm to generate a paragraph-long expert identity that best fulfills the instruction through carefully crafted few-shot demonstrations. then, it directs the llm to respond as the generated expert. however, it can bias the model's response toward the generated expert -a critical weakness (fig. 1 ). nominal group technique (ngt) @xcite . the ngt is a structured decisionmaking process that aids teams in identifying problems and generating solutions. it effectively organizes group ideas, combining individual judgments, particularly useful in scenarios marked by uncertainty or disagreement. widely utilized in business and government, ngt typically involves 4 steps: ngt 1. idea generation. each team member independently writes down their ideas. ngt 2. round-robin idea recording. ideas are shared in a round-robin fashion and recorded for all to see without discussion and elaboration. ngt 3. discussion of the list of ideas. the participants discuss each idea on the list so that they are clear about the meaning of the ideas. ngt 4. voting. members identify key ideas, rank-order preferences (optional), record votes (agreements, conflicts), and discuss the voting. ## multi-expert prompting in deployment, when presented with an input instruction i, an llm m is expected to generate a response a while ensuring informativeness, usefulness, truthfulness, non-toxicity, factuality, and nonhurtfulness. multi-expert prompting is designed for this goal and consists of two steps: (1) experts & responses generation and (2) expert responses aggregation. in the first step, m is instructed to generate n experts {(e 1 , d 1 ), ..., (e n , d n )} with e i as the i-th expert identity and d i as its description. it is then executed n times as each expert to respond to i, offering n long-form expert responses, denoted as {a 1 , . . . , a n }. in the second step, m combines {a 1 , . . . , a n } into a comb and selects the best among a i and a comb as a. the steps' details are below, and our detailed prompts and cost analysis are provided in appx.-c. let us denote g m : v * → v * be the generation function of m where v is the model vocabulary. 2 ) aggregating expert responses ( §3.2). given an input instruction, the first step targets generating expert identities that best fulfill the instruction and expert responses, while the second step focuses on aggregating and selecting the best from individual and combined expert responses. ## evaluation we show that multi-expert prompting greatly improves reliability and safety ( §4.1) and the informativeness and usefulness ( §4.2) over the baselines. baselines. we compare multi-expert prompting with six strong baselines: (b1) zero-shot; (b2) zero-shot-cot @xcite ; (b3) self-refine @xcite which interactively utilizes llms to feedback and refine the response; (b4) universal self-consistency @xcite exist and multiple long-form responses are correct. we collect all open-ended questions from expertqa @xcite consisting of 528 questions in 32 topics. metrics c5 and c6 are computed automatically via the win/draw/lose comparison between multi-expert prompting and other baselines by chatgpt, found to be an effective evaluator @xcite . we include the evaluation prompts in appx.-d. ## human evaluation and analyses human evaluation is essential for assessing the subtask performance of models in multi-expert prompting, as no automated metrics exist for this purpose. we conduct human ## discussion we discuss the underlying reasons for multi-expert prompting's effectiveness and address its design choices. ## conclusion we introduce multi-expert prompting, an efficient method that simulates multiple experts within an llm and aggregates their responses to improve generation. drawing inspiration from the nominal group technique, this approach pioneers in aggregating lengthy responses in llm-powered multi-agent systems by well-studied human-design decision-making frameworks in a single turn. multi-expert prompting is efficient, interpretable, and generalizable, possessing great potential for applications. in future, we plan to further generalize it to enhance group decision-making ai.