ExpertPrompting: Instructing Large Language Models to be Distinguished Experts (2305.14688v1)

Abstract: The answering quality of an aligned LLM can be drastically improved if treated with proper crafting of prompts. In this paper, we propose ExpertPrompting to elicit the potential of LLMs to answer as distinguished experts. We first utilize In-Context Learning to automatically synthesize detailed and customized descriptions of the expert identity for each specific instruction, and then ask LLMs to provide answer conditioned on such agent background. Based on this augmented prompting strategy, we produce a new set of instruction-following data using GPT-3.5, and train a competitive open-source chat assistant called ExpertLLaMA. We employ GPT4-based evaluation to show that 1) the expert data is of significantly higher quality than vanilla answers, and 2) ExpertLLaMA outperforms existing open-source opponents and achieves 96\% of the original ChatGPT's capability. All data and the ExpertLLaMA model will be made publicly available at \url{https://github.com/OFA-Sys/ExpertLLaMA}.

ExpertPrompting: Instructing LLMs to be Distinguished Experts

The paper focuses on ameliorating the performance of LLMs through a novel methodology named "ExpertPrompting." The central premise involves strategically crafting prompts that leverage the latent potential of LLMs, such as GPT-3.5, to deliver responses resembling those of domain-specific experts.

Methodology Overview

ExpertPrompting employs In-Context Learning to automatically generate expert identity descriptions tailored to specific prompts. This approach envisions a suitable expert agent per instruction, conditioning the LLM to respond with enhanced domain-specific expertise. The process involves training the ExpertLLaMA, an open-source chat assistant, on instruction sets augmented by ExpertPrompting techniques. Notably, the model, evaluated using GPT4, is shown to outperform existing open-source counterparts and approximates 96% of ChatGPT's capabilities.

Implications and Findings

Automated and Tailored Prompting:

By automatically generating detailed expert identities, ExpertPrompting alleviates the need for manual prompt engineering while maintaining adaptability across diverse domains. The approach yields more comprehensive and nuanced responses, addressing the variability in outputs resulting from standard prompting techniques.

Evaluation and Results:

The evaluation employs GPT4-based metrics, demonstrating that ExpertPrompting achieves significantly higher quality outputs compared to baseline methods. The paper presents quantitative evidence with 48.5% preference for ExpertPrompting-enhanced responses, compared to 23% for standard prompts.

Training ExpertLLaMA:

The trained chat assistant, ExpertLLaMA, validates the effectiveness of ExpertPrompting by outperforming models such as Vicuna and LLaMA-GPT4, despite relying on GPT-3.5 for data augmentation. This suggests a robust training paradigm that maximizes LLM capabilities through nuanced prompting.

Discussion on Future Directions

The implications of this research extend to optimizing LLM deployment in real-world applications requiring domain-specific knowledge, such as medical advice or legal consultation. Future work could explore scaling the approach to encompass larger datasets beyond the initial 52k Alpaca instructions, enhancing the breadth of expert identities available for generating responses.

Moreover, this research paves the way for further refinement of automated prompting techniques, potentially integrating user feedback loops to continually improve model outputs. Exploring cross-model applications and the transferability of expert identities across different LLM architectures could provide avenues for broader adaptability and impact.

Conclusion

ExpertPrompting represents a significant step forward in aligning LLM outputs with expert-level expectations without extensive manual intervention. The research offers a practical framework for maximizing the proficiency of LLMs in delivering tailored, high-quality responses across various domains, contributing valuable insights into advancing AI-driven communication tools.