Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large language models
Accurately extracting nuanced, contextualized data from research articles is laborious and time intensive. Here, we investigate the performance of frontier, browser-based large language models (LLMs) to extract highly contextualized information. We demonstrate four escalating workflows, 1) given an expert curated prompt and research articles, most frontier LLMs perform well at data extraction, however can struggle with interpreting scientific context and nuance, 2) given simple instructions, LLMs can author their own prompts which were almost as eNective as expert-written prompts, 3) autonomou
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 52%AI for Scientific Literature Mining: PubMed, Semantic Scholar, and What Actually Works - Technology Networks →
- PossiblePossibly related (embedding) · 50%Build a protein research copilot with Amazon Bedrock AgentCore →
- LinkedLinked via arxiv author · 85%Valentin Romanov →
“Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large langu”
- LinkedLinked via arxiv author · 85%Monique Bax →
“Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large langu”
- LinkedLinked via arxiv author · 85%Steven Niederer →
“Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large langu”