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在上一期热点文献推荐中，我们为您推荐了通信领域的最新进展，包括通过模加运算实现太赫兹可编程超表面的高分辨率二维波束控制、用于100-Gbps THz 6G无线传输的拓扑集成天线、基于石墨烯集成光电混频器的亚太赫兹无线传输、6G超大规模MIMO教程：基础、信号处理和应用。本期我们为您选取了4篇文献，介绍AI大语言模型的最新进展，包括来自Google DeepMind的研究团队提出的新方法FunSearch——利用大语言模型的程序搜索解决数学难题、基于大语言模型的科学文本结构化信息提取、大语言模型简化临床研究机器学习、利用大语言模型预测化学。文献一利用大语言模型的程序搜索解决数学难题 Mathematical discoveries from program search with large language models Romera-Paredes, Bernardino, etc. NATURE, 2024, 625(7995): 468–475 Large language models (LLMs) have demonstrated tremendous capabilities in solving complex tasks, from quantitative reasoning to understanding natural language. However, LLMs sometimes suffer from confabulations (or hallucinations), which can result in them making plausible but incorrect statements. This hinders the use of current large models in scientific discovery. Here we introduce FunSearch (short for searching in the function space), an evolutionary procedure based on pairing a pretrained LLM with a systematic evaluator. We demonstrate the effectiveness of this approach to surpass the best-known results in important problems, pushing the boundary of existing LLM-based approaches. Applying FunSearch to a central problem in extremal combinatorics—the cap set problem—we discover new constructions of large cap sets going beyond the best-known ones, both in finite dimensional and asymptotic cases. This shows that it is possible to make discoveries for established open problems using LLMs. We showcase the generality of FunSearch by applying it to an algorithmic problem, online bin packing, finding new heuristics that improve on widely used baselines. In contrast to most computer search approaches, FunSearch searches for programs that describe how to solve a problem, rather than what the solution is. Beyond being an effective and scalable strategy, discovered programs tend to be more interpretable than raw solutions, enabling feedback loops between domain experts and FunSearch, and the deployment of such programs in real-world applications. 阅读原文：https://www.nature.com/articles/s41586-023-06924-6 文献二基于大语言模型的科学文本结构化信息提取 Structured information extraction from scientific text with large language models Dagdelen, John, etc. NATURE COMMUNICATIONS, 2024, 15 Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers. 阅读原文：https://www.nature.com/articles/s41467-024-45563-x 文献三大语言模型简化临床研究机器学习 Large language models streamline automated machine learning for clinical studies Arasteh, Soroosh Tayebi, etc. NATURE COMMUNICATIONS, 2024, 15 A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical data analysis. We investigated the potential of the ChatGPT Advanced Data Analysis (ADA), an extension of GPT-4, to bridge this gap and perform ML analyses efficiently. Real-world clinical datasets and study details from large trials across various medical specialties were presented to ChatGPT ADA without specific guidance. ChatGPT ADA autonomously developed state-of-the-art ML models based on the original study’s training data to predict clinical outcomes such as cancer development, cancer progression, disease complications, or biomarkers such as pathogenic gene sequences. Following the re-implementation and optimization of the published models, the head-to-head comparison of the ChatGPT ADA-crafted ML models and their respective manually crafted counterparts revealed no significant differences in traditional performance metrics (p ≥ 0.072). Strikingly, the ChatGPT ADA-crafted ML models often outperformed their counterparts. In conclusion, ChatGPT ADA offers a promising avenue to democratize ML in medicine by simplifying complex data analyses, yet should enhance, not replace, specialized training and resources, to promote broader applications in medical research and practice. 阅读原文：https://www.nature.com/articles/s41467-024-45879-8 文献四利用大语言模型预测化学 Leveraging large language models for predictive chemistry Jablonka, Kevin Maik, etc. NATURE MACHINE INTELLIGENCE, 2024, 6 Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemical knowledge for each application and, therefore, require specialized expertise to develop. Here we show that GPT-3, a large language model trained on vast amounts of text extracted from the Internet, can easily be adapted to solve various tasks in chemistry and materials science by fine-tuning it to answer chemical questions in natural language with the correct answer. We compared this approach with dedicated machine learning models for many applications spanning the properties of molecules and materials to the yield of chemical reactions. Surprisingly, our fine-tuned version of GPT-3 can perform comparably to or even outperform conventional machine learning techniques, in particular in the low-data limit. In addition, we can perform inverse design by simply inverting the questions. The ease of use and high performance, especially for small datasets, can impact the fundamental approach to using machine learning in the chemical and material sciences. In addition to a literature search, querying a pre-trained large language model might become a routine way to bootstrap a project by leveraging the collective knowledge encoded in these foundation models, or to provide a baseline for predictive tasks. 阅读原文：https://www.nature.com/articles/s42256-023-00788-1 “前沿文献专题推荐服务”是图书馆情报服务部学科服务的重要内容。欢迎有相关需求的科研人员与我们联系，我们将为您提供更有针对性的定题服务。邮箱：yandong80@bupt.edu.cn 联系人：杨老师闫老师邮箱：yandong80@bupt.edu.cn 电话：西土城校区图书馆125室 62281933 62283502 沙河校区图书馆东205室 66605330 66605331

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