LitSumm：用于非编码RNA文献综述的大语言模型
LitSumm: Large language models for literature summarisation of non-coding RNAs
AI总结：
针对生命科学文献整理人力不足的问题，本研究提出LitSumm方法，借助商用大语言模型与提示-检查链生成非编码RNA的高质量文献综述，已应用于超4600种ncRNA并通过RNAcentral公开结果。
摘要：
生命科学领域的文献整理是一项日益严峻的挑战。论文发表速度持续攀升，加之全球整理人员数量相对固定，给生物医学知识库的开发者带来了重大难题。极少有知识库具备覆盖全部相关文献的资源，所有机构都必须对工作优先级进行排序。\n在本研究中，我们通过利用大语言模型（LLMs）生成非编码RNA的文献综述，为缓解RNA科学领域整理人员时间不足的问题迈出了第一步。我们证明，借助商用LLM以及提示词与检查链式流程，能够从文献中自动生成高质量、事实准确且参考文献无误的综述。我们对部分综述开展了人工评估，其中大多数被评定为极高质量。\n我们将该工具应用于超过4600种ncRNA的精选集合，并通过RNAcentral资源公开生成的综述。我们得出结论：只要采用精心设计的提示词和自动化检查机制，利用当前一代LLMs实现自动化文献综述是可行的。
英文摘要：
Curation of literature in life sciences is a growing challenge. The continued increase in the rate of publication, coupled with the relatively fixed number of curators worldwide presents a major challenge to developers of biomedical knowledgebases. Very few knowledgebases have resources to scale to the whole relevant literature and all have to prioritise their efforts. In this work, we take a first step to alleviating the lack of curator time in RNA science by generating summaries of literature for non-coding RNAs using large language models (LLMs). We demonstrate that high-quality, factually accurate summaries with accurate references can be automatically generated from the literature using a commercial LLM and a chain of prompts and checks. Manual assessment was carried out for a subset of summaries, with the majority being rated extremely high quality. We apply our tool to a selection of over 4,600 ncRNAs and make the generated summaries available via the RNAcentral resource. We conclude that automated literature summarization is feasible with the current generation of LLMs, provided careful prompting and automated checking are applied.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
