REVIEW 10 cited by
ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
read the original abstract
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.
Forward citations
Cited by 10 Pith papers
-
The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment
An AI-agent social platform generated mostly neutral content whose use in fine-tuning reduced model truthfulness comparably to human Reddit data, suggesting limited unique harm but flagging tail risks like secret leaks.
-
Automated Design of Agentic Systems
Meta Agent Search uses a meta-agent to iteratively program novel agentic systems in code, producing agents that outperform state-of-the-art hand-designed ones across coding, science, and math while transferring across...
-
Understanding the Mechanism of Altruism in Large Language Models
A small set of sparse autoencoder features in LLMs drives shifts between generous and selfish allocations in dictator games, with causal patching and steering confirming their role and generalization to other social games.
-
RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models
RankFlow deploys four LLM roles in sequence to rewrite queries, generate pseudo-answers, summarize passages, and rerank candidates, outperforming prior methods on TREC-DL, BEIR, and NovelEval.
-
SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs
SLIP combines a soft label mechanism with key-extraction-guided CoT to reduce instruction backdoor attack success rate to 25.13% and raise clean accuracy to 87.15% in LLM agents.
-
Teaching Astronomy with Large Language Models
Structured integration of LLMs in astronomy education, including a domain-specific tutor and documentation requirements, leads to improved AI literacy and reduced student reliance on AI over the semester.
-
Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs
Humans exhibit greater source-label bias in logical fallacy judgments than LLMs, which maintain more consistent evaluations regardless of source cues.
-
Using Large Language Models in Physics Education
Frontier LLMs from mid-2024 to late-2025 reach near-perfect scores on text-based physics problems and show improved human alignment in grading, but assigning partial credit for flawed reasoning remains difficult.
-
Using Large Language Models in Physics Education
Frontier LLMs from late 2025 reach near-perfect scores on text-based physics problem solving and show improved human-grading alignment, yet still struggle to assign partial credit for flawed reasoning.
-
Dr. Jekyll and Mr. Hyde: Two Faces of LLMs
Impersonating complex misaligned personas via biographies and role-play bypasses safety in ChatGPT, Gemini, and Deepseek, succeeding on 38-40 out of 40 illicit questions across tested models.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.