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Source note3 min read

Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large language models

Source

arXiv

arxiv.org/abs/2608.19025v1

Read the source

Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Valentin Romanov, Monique Bax, Steven Niederer
  • Venue: arXiv: cs.AI
  • Published: 2026-08-19
  • Collected: 2026-08-20
  • URL: http://arxiv.org/abs/2608.19025v1
  • PDF: https://arxiv.org/pdf/2608.19025v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

The paper investigates the performance of large language models (LLMs) in extracting contextualized information from research articles, finding that while LLMs can perform well with expert-curated prompts, they may struggle with interpreting scientific context and nuance. The authors also explore methods for LLMs to author their own prompts, autonomously discover literature, and create new datasets. The findings suggest a practical approach to scaling scientific data curation while maintaining expert oversight.

What the Document Says

  • The paper discusses the challenges in accurately extracting contextualized information from research articles.
  • It investigates the performance of large language models (LLMs) for this task.
  • The authors present four workflows for LLM data extraction.
  • In the first workflow, LLMs perform well but struggle with interpreting scientific context and nuance.
  • In the second workflow, LLMs can author their own prompts which are nearly as effective as expert-written prompts.
  • The third workflow involves autonomous discovery of research literature by LLMs, which is difficult as agents often miss or hallucinate references.
  • In the fourth workflow, LLMs create new datasets from published guidelines that closely match human-expert judgments, but still require a human-in-the-loop.

Key Concepts Mentioned

  • large language models
  • data extraction
  • scientific literature
  • workflows
  • contextualized information

Claims Made by the Document

  • LLMs can perform well at data extraction from research articles.
  • LLMs may struggle with interpreting scientific context and nuance.
  • LLMs can author their own prompts which are nearly as effective as expert-written prompts.
  • Autonomous discovery of research literature by LLMs is difficult due to missed or hallucinated references.
  • LLMs can create new datasets from published guidelines that closely match human-expert judgments.

Evidence Used

  • The results and discussions in the paper

Methods / Approach

  • Investigation of large language models' performance in data extraction
  • Presentation of four workflows for LLM data extraction

Limitations Stated by the Authors

  • The authors acknowledge that their findings are based on a specific set of experiments and may not generalize to all scenarios or types of research articles.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • As the agent does not have access to the actual experimental setup, it cannot assess the generalizability of the findings beyond the specific conditions described in the paper.
  • The agent also lacks the ability to interpret the quality and validity of the experiments conducted by the authors.

Open Questions

  • What are the potential implications of this research for scaling scientific data curation?
  • How can the difficulties encountered in autonomous discovery of research literature by LLMs be addressed?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper provides a practical approach to scaling scientific data curation, but it does not discuss the potential costs or challenges associated with this approach.
  • The findings suggest that human oversight is still necessary for LLM-assisted data extraction, but the paper does not delve into the specifics of how this could be implemented in practice.

Raw Abstract / Excerpt

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) autonomous discovery of research literature was diNicult, agents either missed or hallucinated references, and 4) LLMs can create new datasets from published guidelines that closely match human-expert judges, but still require a human-in-the-loop. Together, these findings define an auditable division of labour in which experts specify the evidence standard, models cross-check repeated extractions and researchers resolve disputed cases, providing a practical route to scaling scientific data curation without relinquishing expert oversight.