Multi-Expert Prompting: Enhancing Large Language Model Generation Through Simulated Expert Collaboration and Response Aggregation

Core Concepts

Multi-expert prompting significantly improves the reliability, safety, and usefulness of large language models by simulating multiple expert perspectives, aggregating their responses, and selecting the best answer through a novel, seven-subtask method inspired by the Nominal Group Technique.

Abstract

• Bibliographic Information: Do Xuan Long, Duong Ngoc Yen, Luu Anh Tuan, Kenji Kawaguchi, Min-Yen Kan, & Nancy F. Chen. (2024). Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models. arXiv preprint arXiv:2411.00492v1.
• Research Objective: This paper introduces Multi-expert Prompting, a novel prompting technique designed to enhance the quality of large language model (LLM) generated responses by simulating multiple expert perspectives and aggregating their responses.
• Methodology: The researchers developed a two-step process. First, given an input instruction, the LLM generates multiple expert identities with concise role descriptions. The LLM then answers the instruction from the perspective of each generated expert. Second, the LLM aggregates the individual expert responses into a combined response and selects the best answer among the individual and combined responses using a seven-subtask method based on the Nominal Group Technique. The researchers evaluated their method on various benchmarks for truthfulness, factuality, toxicity, hurtfulness, informativeness, and usefulness, comparing it against several baseline prompting techniques.
• Key Findings: Multi-expert Prompting significantly outperformed all baseline prompting techniques across all evaluation metrics. Notably, it achieved state-of-the-art truthfulness on the TruthfulQA benchmark using ChatGPT, surpassing the previous best result by 8.69%. The researchers also conducted human evaluations, which confirmed the effectiveness of Multi-expert Prompting in generating more informative and useful responses compared to the baselines.
• Main Conclusions: Simulating multiple expert perspectives and aggregating their responses through a structured decision-making process significantly improves the quality of LLM-generated text. This approach enhances the reliability, safety, and usefulness of LLMs, making them more aligned with human intentions.
• Significance: This research contributes a novel and effective prompting technique for improving LLM generation quality. The proposed Multi-expert Prompting method addresses limitations of existing techniques relying on single expert perspectives, demonstrating the potential of leveraging multi-agent systems and human-designed decision-making frameworks in LLM prompting.
• Limitations and Future Research: The authors acknowledge limitations regarding the equal weighting of expert opinions and potential for LLM hallucination of expert identities. Future research directions include exploring weighted aggregation methods and addressing potential biases in expert generation.

Stats

Multi-expert Prompting outperforms the best baseline by 8.69% on truthfulness with ChatGPT on the TruthfulQA benchmark.
Multi-expert Prompting generates significantly more informative responses, with a 75% win rate on average, compared to baselines on the ExpertQA dataset.
Multi-expert Prompting generates significantly more useful responses, with a 76.5% win rate on average, compared to baselines on the ExpertQA dataset.
LLMs using Multi-expert Prompting select the aggregated response over individual expert responses in over 90% of test cases.

Quotes

"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."

Key Insights Distilled From

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

by Do Xuan Long... at arxiv.org 11-04-2024

https://arxiv.org/pdf/2411.00492.pdf

Deeper Inquiries

How can Multi-expert Prompting be adapted for other NLP tasks beyond question answering, such as summarization or dialogue generation?

Multi-expert Prompting, with its innovative approach to leveraging diverse perspectives within a single LLM, holds significant potential for adaptation to various NLP tasks beyond question answering. Here's how:
1. Summarization:

Expert Identification: Instead of diverse fields, experts could represent different summarization styles (e.g., abstractive, extractive, keyphrase-based). Each "expert" would generate a summary tailored to its assigned style.
Subtask Adaptation: Subtasks would focus on identifying key information (S1), resolving conflicting summaries of the same information (S2, S3), highlighting unique points from each summary (S4), and ultimately combining these into a comprehensive and informative final summary (S5-S7).
2. Dialogue Generation:

Persona-based Experts: Experts could embody distinct personas relevant to the conversation (e.g., customer service agent, technical expert, casual conversationalist). Each expert would contribute dialogue turns aligned with its persona.
Dynamic Subtask Adjustment: Subtasks would need to be more dynamic, potentially involving turn-taking, maintaining coherence across turns, and ensuring the dialogue progresses naturally. Conflict resolution might involve blending different conversational styles or selecting the most contextually appropriate response.
Key Considerations for Adaptation:

Task-Specific Expert Roles: Defining expert roles relevant to the specific NLP task is crucial.
Subtask Refinement: Adapting or creating new subtasks to align with the task's objectives and output structure is essential.
Evaluation Metrics: Utilizing appropriate evaluation metrics for the target task is necessary to assess the effectiveness of the adapted Multi-expert Prompting framework.

Could the reliance on a pre-defined set of subtasks for response aggregation limit the flexibility and adaptability of Multi-expert Prompting in dynamic or open-ended conversational contexts?

Yes, the current reliance on a fixed set of subtasks in Multi-expert Prompting's response aggregation process could potentially limit its flexibility and adaptability, especially in dynamic conversational contexts. Here's why:

Linearity of Subtasks: The current subtasks (S1-S7) follow a relatively linear and pre-defined sequence. This structure might not be suitable for the non-linear, unpredictable nature of open-ended dialogues where new topics emerge, and the conversation flow is dynamic.
Lack of Contextual Awareness: The existing subtasks primarily focus on content aggregation and conflict resolution without explicitly considering the broader conversational context. In a dialogue, the relevance and importance of information can shift rapidly, requiring a more context-aware aggregation approach.
Limited Handling of Disagreement: While the current framework addresses conflicting viewpoints, it might not be equipped to handle situations where a clear consensus isn't achievable or desirable. In some conversations, acknowledging and exploring disagreements can be more valuable than forcing a resolution.
Potential Solutions for Enhanced Flexibility:

Dynamic Subtask Selection: Instead of a fixed sequence, subtasks could be selected or prioritized dynamically based on the evolving dialogue context.
Contextualized Subtask Execution: Incorporating mechanisms for subtasks to access and utilize conversational history, user preferences, and other contextual cues would enhance adaptability.
Integration of Dialogue Management Strategies: Combining Multi-expert Prompting with dialogue management techniques could enable more sophisticated turn-taking, topic handling, and response selection.

What are the potential implications of using AI systems that simulate human expertise for decision-making in sensitive domains like healthcare or law, and how can we ensure responsible and ethical use?

The use of AI systems simulating human expertise in sensitive domains like healthcare and law presents both promising opportunities and significant ethical challenges.
Potential Benefits:

Increased Access to Expertise: AI could democratize access to specialized knowledge, potentially benefiting underserved communities or individuals in remote areas.
Efficiency and Speed: AI can process vast amounts of data and assist human experts in making faster and more informed decisions.
Reduced Human Bias: In some cases, AI systems might exhibit less bias than humans, leading to fairer outcomes.
Ethical Concerns:

Bias Amplification: If trained on biased data, AI systems can perpetuate and even amplify existing societal biases, leading to unfair or discriminatory outcomes.
Lack of Transparency: The decision-making processes of complex AI models can be opaque, making it difficult to understand the reasoning behind their recommendations.
Over-reliance and Deskilling: Over-dependence on AI could lead to a decline in human expertise and critical thinking skills.
Accountability and Liability: Determining responsibility in case of errors or harm caused by AI-driven decisions remains a complex issue.
Ensuring Responsible and Ethical Use:

Rigorous Testing and Validation: Thorough testing on diverse datasets and real-world scenarios is crucial to identify and mitigate biases and ensure accuracy.
Transparency and Explainability: Developing AI models that provide clear explanations for their decisions is essential for building trust and accountability.
Human Oversight and Control: Maintaining human oversight in critical decision-making processes is crucial to prevent unintended consequences.
Continuous Monitoring and Evaluation: Regularly monitoring AI systems for bias, accuracy, and ethical implications is essential for responsible deployment.
Ethical Guidelines and Regulations: Establishing clear ethical guidelines and regulations for developing and deploying AI in sensitive domains is paramount.
In conclusion, while AI holds immense potential for positive impact in healthcare and law, its ethical implications must be carefully considered. A multi-disciplinary approach involving AI developers, domain experts, ethicists, and policymakers is crucial to ensure responsible and beneficial use of these powerful technologies.