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ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

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arxiv 2305.14688 v2 pith:7LIH3W5C submitted 2023-05-24 cs.CL cs.AI

Benfeng Xu , An Yang , Junyang Lin , Quan Wang , Chang Zhou , Yongdong Zhang , Zhendong Mao This is my paper · ORCID
classification cs.CLcs.AI
keywords expertllamadataanswerdistinguishedexpertexpertpromptingexpertslanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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The answering quality of an aligned large language model (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 https://github.com/OFA-Sys/ExpertLLaMA.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 53 citations worldwide. Full citation record

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    cs.CL 2026-01 conditional novelty 6.0 of 10

    XCR-Bench provides 4,100+ parallel sentences with 1,098 culture-specific items mapped to Hall's Triad, and shows LLMs struggle most with deeper, semi-visible cultural norms.

  2. Exploring Advanced LLM Multi-Agent Systems Based on Blackboard Architecture

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    A blackboard-based LLM multi-agent system with controller-selected agents achieves competitive benchmark accuracy at lower token cost than several static and dynamically optimized baselines.

  3. Integrating gender inclusivity into large language models via instruction tuning

    cs.CL 2025-08 reject novelty 5.0 of 10

    Abstract promises gender-inclusive Polish LLM tuning with the IPIS dataset, while the full text is an unrelated quantum transformer paper; no evidence for the declared claims is present.

  4. An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis

    cs.AI 2025-07 conditional novelty 4.0 of 10

    A prompt-plus-knowledge-graph framework for legal dispute analysis reports improved LLM sensitivity and citation accuracy on a 100-pair test set, but with limited statistical support.

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