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，并且超越了自然语言处理（NLP）。特别是，人们对如何利用这些模型可以利用并提高这些模型，以实现跨科学学科的科学探究。该奖项将支持一个专注于利用LLM的生物发现的研讨会组织：LLMS4BIO涉及2024年AAAI会议。 AAAI会议系列已确立自己是AI世界首屈一指的研究会议。该研讨会解决了这些挑战，并将计算机科学，信息科学以及分子，细胞和系统生物学的研究人员汇集在一起​​，以应对推进生物学发现的独特挑战。该研讨会活动将增加学生参加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，通过向美国的学生提供旅行赠款。 AAAI会议系列已确立自己是AI世界首屈一指的研究会议。结果包括新问题空间的公式，在已确定的交叉社区中包含更多的研究人员，以及催化有关可访问和包容性LLM的进一步创新，以为下一个科学突破提供动力。在会议上，美国研究人员的强烈代表也有助于保持美国在这一重要领域的竞争力。该研讨会活动将为展示AI技术进步和社会领域的应用提供一个国际论坛。会议涵盖了AI的各个方面，包括理论，算法，软件，系统以及应用程序。该奖项将支持：i）“生物学的LLM”小组讨论，ii）纸质演示和问答互动，iii）跨NLP和生物领域专家的网络，以及iv）我们社区未来机会的前景。这些活动将在学生准备促进科学研究中的职业并扩大代表性不足的群体参与计算，尤其是在AI研究中的参与时，将支持他们的职业生涯。 LLMS研究的当前研究空间使研究人员挑战了深入了解科学问题，可接受的标准，数据集，指标和基准任务，这些任务确实捕捉了我们在问题上的发展能力，并且在数十年的艰苦研究中收集了宝贵的知识。当前的封闭或描述性不佳的行业模型的趋势仍然超出了学术研究人员通常可用的资源，通常通过非Per审核平台传播，也不是进行研究的交叉侵入。该研讨会解决了这些挑战，并将计算机科学，信息科学以及分子，细胞和系统生物学的研究人员汇集在一起​​，以应对推进生物学发现的独特挑战，例如标准化数据集，社区所接受的基准，实验性噪声和不确定的噪声和不确定性量化，解释，解释，并受到了先前的生物学知识的伤害。涉及研讨会活动的GitHub项目https://github.com/llms4science-community，该项目提供了一个长期平台，可为共享研讨会的研究文章，数据集，基准，基准标准，指标以及其他在此期间的宣传授权的授权的授权和其他授权的奖励的知识。优点和更广泛的影响审查标准。