Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large language models· 自提示和跨模型共识
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,
自提示和跨模型共识使大语言模型能从科研文献中高效提取数据。
- 核心方法
- 研究了前沿的大语言模型通过专家设计的提示、自动生成的提示以及跨模型共识在数据提取上的表现。
- 适合谁读
- 适合研究者和工程师阅读，了解大语言模型在科研数据提取中的应用和技术细节。
- 要解决的问题
- 准确提取科研文章中的细微、上下文相关数据耗时且繁重。
- 关键实验
- 涉及四种逐步复杂的工作流程实验，包括专家提示、模型自提示、自主发现文献和创建新数据集。
- 主要贡献
- 展示了大语言模型在数据提取任务上的潜力，特别是通过自提示和跨模型共识提高了可重复性和准确性。
- 意义与局限
- 该研究的意义在于提高了大语言模型处理复杂科研文献的能力，但模型在科学语境和细微差异的解释上仍有局限。