SciSoc LLM Workshop: Large Language Models for Scientific and Societal Advances
The proposed ''SciSoc LLM Workshop: Large Language Models for Scientific and Societal Advances'' aims to explore the profound implications and potential of Large Language Models (LLMs) in driving forward scientific inquiry and addressing critical societal challenges. As LLMs such as GPT-4 continue to redefine boundaries in both complexity and capability, their integration into the scientific and societal domains is not just beneficial but essential. In particular, LLMs have demonstrated substantial value in improving our understanding of complex datasets and generating insights across various fields such as healthcare, environmental science, education, and public policy. By bringing together experts and enthusiasts from diverse fields, the workshop aims to foster a comprehensive understanding of how LLMs can redefine traditional research methodologies. Participants will explore innovative ways to harness the power of LLMs for greater efficiency and innovation in their respective fields, potentially catalyzing a new era of scientific and societal advancement.
Authors
- Danai Koutra (ORCID: https://orcid.org/0000-0002-3206-8179)
- Wenpeng Yin (ORCID: https://orcid.org/0000-0002-3154-1139)
- Lu Cheng (ORCID: https://orcid.org/0000-0002-2503-2522)
- Qingsong Wen (ORCID: https://orcid.org/0000-0003-4516-2524)
- B. Aditya Prakash (ORCID: https://orcid.org/0000-0002-3252-455X)
- Wei Jin (ORCID: https://orcid.org/0000-0002-5054-954X)
- Yan Liu (ORCID: https://orcid.org/0000-0002-7055-9518)
- Xianfeng Tang (ORCID: https://orcid.org/0000-0003-1554-2761)
Institutions
- University of Southern California (US)
- Georgia Institute of Technology (US)
- Pennsylvania State University (US)
- Emory University (US)
- University of Michigan (US)
- University of Illinois Chicago (US)
- Film Independent (US)
Publication Details
- Published
- 2025-08-03
- DOI
- https://doi.org/10.1145/3711896.3737863
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- National Institutes of Health
- Centers for Disease Control and Prevention