MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
New York University Shanghai（上海纽约大学）；Xiamen University（厦门大学）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Zhejiang University（浙江大学）；The Hong Kong Polytechnic University（香港理工大学）
Yuyan Liu, Sirui Ding, Sheng Zhou, Wenqi Fan, Qiaoyu Tan
Molecular property prediction (MPP) is a fundamental and crucial task in drug discovery. However, prior methods are limited by the requirement for a large number of labeled molecules and their restricted ability to generalize for unseen and new tasks, both of which are essential for real-world applications. To address these challenges, we present MolecularGPT for few-shot MPP. From a perspective on instruction tuning, we fine-tune large language models (LLMs) based on curated molecular instructions spanning over 1000 property prediction tasks. This enables building a versatile and specialized LLM that can be adapted to novel MPP tasks without any fine-tuning through zero- and few-shot in-context learning (ICL). MolecularGPT exhibits competitive in-context reasoning capabilities across 10 downstream evaluation datasets, setting new benchmarks for few-shot molecular prediction tasks. More importantly, with just two-shot examples, MolecularGPT can outperform standard supervised graph neural network methods on 4 out of 7 datasets. It also excels state-of-the-art LLM baselines by up to 15.7% increase on classification accuracy and decrease of 17.9 on regression metrics (e.g., RMSE) under zero-shot. This study demonstrates the potential of LLMs as effective few-shot molecular property predictors. The code is available at https://github.com/NYUSHCS/MolecularGPT.
LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations
School of Computer Science, Beihang University（北京航空航天大学计算机学院）；School of Materials Science & Engineering, University of Science and Technology Beijing（北京科技大学材料科学与工程学院）；Westlake University（西湖大学）
LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction
Princeton University（普林斯顿大学）；Vertaix；Acceleration Consortium, University of Toronto（多伦多大学加速联盟）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Schwartz Reisman Institute for Technology and Society（施瓦茨·赖斯曼技术与社会研究所）；University of Toronto（多伦多大学）
Bidirectional Language Models Are Also Few-shot Learners
University of Pennsylvania（宾夕法尼亚大学）；Microsoft（微软）；Google Research（谷歌研究院）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）
大语言模型，例如GPT-3（Brown等人，2020）可以执行任意任务，而无需在仅使用几个标记示例进行提示后进行微调。一个任意的任务可以被重新表述为一个自然语言提示，一个语言模型可以被要求生成完成，间接地执行任务的范式称为基于语义的学习。到目前为止，基于突现的学习能力主要是针对单向语言模型来证明的。然而，在去噪目标上预训练的双向语言模型（例如掩蔽语言建模）为迁移学习产生更强的学习表示。这激发了促进双向模式的可能性，但它们的预培训目标使它们在很大程度上与现有的激励范式不相容。我们提出了SAP（顺序自回归拟合），一种技术，使双向模型的提示。利用机器翻译任务作为案例研究，我们提出了双向mT5模型（Xue et al.，2021）与SAP进行比较，并证明其Few-Shot和zero-shot转换优于单向模型（如GPT-3和XGLM）的Few-Shot转换（Lin et al.，2021），尽管mT5的参数减少了约50%。我们进一步表明SAP在问答和摘要方面是有效的。这是第一次，我们的研究结果表明，基于语义的学习是一个更广泛的一类语言模型，而不仅仅是单向模型的新兴属性。
IPEval: A Bilingual Intellectual Property Agency Consultation Evaluation Benchmark for Large Language Models
School of Future Technology, Dalian University of Technology（大连理工大学未来技术学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；School of Public Administration and Policy, Dalian University of Technology（大连理工大学公共管理与政策学院）
Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization
Department of Computer Science and Engineering, The Ohio State University, USA（计算机科学与工程系，俄亥俄州立大学）；Translational Data Analytics Institute, The Ohio State University, USA（转化数据分析研究所，俄亥俄州立大学）；Department of Biomedical Informatics, The Ohio State University, USA（生物医学信息学系，俄亥俄州立大学）；College of Pharmacy, The Ohio State University, USA（药学院，俄亥俄州立大学）
GeLLMO: Generalizing Large Language Models for Multi-property Molecule Optimization
Department of Computer Science and Engineering, The Ohio State University, USA（计算机科学与工程系，俄亥俄州立大学）；Translational Data Analytics Institute, The Ohio State University, USA（转化数据分析研究所，俄亥俄州立大学）；Department of Biomedical Informatics, The Ohio State University, USA（生物医学信息学系，俄亥俄州立大学）；College of Pharmacy, The Ohio State University, USA（药学院，俄亥俄州立大学）
Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation
Saarland University（萨尔兰大学）；University of Cambridge（剑桥大学）；Bar-Ilan University（巴伊兰大学）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Washington（华盛顿大学）
IPBench: Benchmarking the Knowledge of Large Language Models in Intellectual Property
Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China（深圳高性能数据挖掘重点实验室，深圳先进技术研究院，中国科学院，中国）；University of Chinese Academy of Sciences, China（中国科学院大学，中国）；Dalian University of Technology, China（大连理工大学，中国）；School of Biomedical Engineering, UNSW Sydney, Australia（生物医学工程学院，新南威尔士大学悉尼分校，澳大利亚）；Shenzhen MSU-BIT University, China（深圳MSU-BIT大学，中国）；Shenzhen University of Advanced Technology, China（深圳大学先进技术学院，中国）
Zero-Shot Detection of LLM-Generated Code via Approximated Task Conditioning
Department of Computer Science, Ben-Gurion University of the Negev（本·古里安大学纳粹夫学院计算机科学系）；Data Science Research Center, Ben-Gurion University of the Negev（本·古里安大学纳粹夫数据科学研究中心）；Tel-Aviv University（特拉维夫大学）；Deep Instinct
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction
Institute of Cyberspace Security, Zhejiang University of Technology（浙江工业大学网络空间安全研究院）；Collaborative Innovation Center of Yangtze RiverDelta Region Green Pharmaceuticals（长三角绿色制药协同创新中心）
From Papers to Property Tables: A Priority-Based LLM Workflow for Materials Data Extraction
Department of Applied Mathematics and Statistics, Johns Hopkins University（约翰霍普金斯大学应用数学与统计系）；Department of Mechanical Engineering, Johns Hopkins University（约翰霍普金斯大学机械工程系）
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Department of Computing, Imperial College London（计算系，帝国理工学院伦敦分校）；Department of Chemistry, Imperial College London（化学系，帝国理工学院伦敦分校）；SOLVE Chemistry（SOLVE化学）
