Collaborative Research: Conference: Large Language Models for Biological Discoveries (LLMs4Bio)

合作研究：会议：生物发现的大型语言模型 (LLMs4Bio)

• 批准号: 2411529
• 负责人: Amarda Shehu
• 金额: $ 1.95万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2411529&HistoricalAwards=false
• 关键词: Collaborative; Research; Conference; Large; Language

Computational research on large language models (LLMs) is advancing rapidly and expanding beyond Natural Language Processing (NLP). In particular, there is great interest in how these models can be leveraged and advanced to enable scientific inquiry across scientific disciplines. This award will support the organization of a workshop focused on the utilization of LLMs for biological discoveries: LLMs4Bio accompanies the 2024 AAAI conference. The AAAI conference series has established itself as the world’s premier research conference in AI. This workshop addresses these challenges and brings together researchers from computer science, information science, and molecular, cellular, and systems biology to address unique challenges in advancing biological discoveries. This workshop activity will increase student participation in the 2024 AAAI Conference on Artificial Intelligence (AAAI) that will take place from Feb 22-28 in Vancouver, Canada, by providing travel grants to U.S.-based students. The AAAI conference series has established itself as the world’s premier research conference in AI. Outcomes include the formulation of new problem spaces, the inclusion of more researchers in the identified intersectional communities, and the catalysis of further innovation on accessible and inclusive LLMs to power the next scientific breakthroughs. A strong representation of US researchers at the conference also helps maintain US competitiveness in this important area.This workshop activity will provide an international forum for presentation of AI technological advancement and applications in societal areas. The conference covers all aspects of AI, including theories, algorithms, software, and systems, and applications. The award will support: i) “LLMs for biology” panel discussions, ii) paper presentation and Q&A interactions, iii) networking across NLP and biological domain experts, and iv) outlook of future opportunities of our communities. These activities will support students and young researchers as they prepare to advance their careers in scientific research, as well as broaden the participation of under-represented groups in computing and, in particular, in AI research. The current pace of research in LLMs makes it challenging for researchers to deeply understand scientific problems, community-acceptable standards, datasets, metrics, and benchmark tasks that truly capture our ability to advance on a problem, and precious knowledge gathered over decades of hard-fought research. The current trend of closed or poorly-described industry models that remain beyond resources typically available to academic researchers, often disseminated through non-peer reviewed platforms is also not conducive to cross-fertilization of research. This workshop addresses these challenges and brings together researchers from computer science, information science, and molecular, cellular, and systems biology to address unique challenges in advancing biological discoveries, such as standardized datasets, community-accepted benchmarks, experimental noise and uncertainty quantification, interpretation, and injection of prior biological knowledge. A Github project, https://github.com/LLMs4Science-Community, that accompanies the workshop activity provides a long-term platform for sharing workshop research articles, datasets, benchmarks, metrics, and other resulting knowledge for the workshop in this debut offering and other planned annual offerings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大型语言模型（LLM）的计算研究正在迅速发展，并超越了自然语言处理（NLP）。特别是，人们对如何利用和推进这些模型以实现跨学科的科学探究非常感兴趣。该奖项将支持组织一个专注于利用LLM进行生物发现的研讨会：LLMs 4 Bio伴随着2024年AAAI会议。AAAI系列会议已成为世界上首屈一指的人工智能研究会议。本次研讨会解决了这些挑战，并汇集了来自计算机科学，信息科学，分子，细胞和系统生物学的研究人员，以解决推进生物学发现的独特挑战。本次研讨会活动将增加学生参与2024年AAAI人工智能会议（AAAI），该会议将于2月22日至28日在加拿大温哥华举行，通过向美国提供旅行赠款。基于学生。AAAI系列会议已成为世界上首屈一指的人工智能研究会议。其成果包括制定新的问题空间，在确定的交叉社区中纳入更多的研究人员，以及催化可访问和包容性LLM的进一步创新，以推动下一个科学突破。美国研究人员在会议上的强大代表性也有助于保持美国在这一重要领域的竞争力。本次研讨会活动将提供一个国际论坛，介绍人工智能技术的进步和在社会领域的应用。会议涵盖了人工智能的各个方面，包括理论，算法，软件和系统以及应用。该奖项将支持：i）“LLM生物学”小组讨论，ii）论文演示和问答互动，iii）NLP和生物领域专家的网络，以及iv）我们社区未来机会的展望。这些活动将为学生和年轻研究人员提供支持，帮助他们准备推进科学研究事业，并扩大代表性不足的群体对计算，特别是人工智能研究的参与。LLM目前的研究速度使研究人员难以深入了解科学问题，社区可接受的标准，数据集，指标和基准任务，这些任务真正抓住了我们推进问题的能力，以及数十年来收集的宝贵知识。目前的趋势是封闭的或描述不佳的行业模型仍然超出了学术研究人员通常可用的资源，通常通过非同行评审的平台传播，这也不利于研究的交叉施肥。本次研讨会解决了这些挑战，并汇集了来自计算机科学，信息科学，分子，细胞和系统生物学的研究人员，以解决推进生物学发现的独特挑战，如标准化数据集，社区接受的基准，实验噪声和不确定性量化，解释和注入先前的生物学知识。Github项目，https：//github.com/LLMs4Science-Community，伴随着研讨会活动，提供了一个长期的平台，用于分享研讨会的研究文章，数据集，基准，指标，以及其他由此产生的知识，为研讨会在这个首次提供和其他计划的年度提供。这个奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。