This workshop seeks to expedite efforts at the intersection of Symbolic Knowledge and Statistical Knowledge inherent in LLMs. The objective is to establish quantifiable methods and acceptable metrics for addressing consistency, reliability, and safety in LLMs. Simultaneously, we seek unimodal or multimodal NeuroSymbolic solutions to mitigate LLM issues through context-aware explanations and reasoning. The workshop also focuses on critical applications of LLMs in health informatics, biomedical informatics, crisis informatics, cyber-physical systems, and legal domains. We invite submissions that present novel developments and assessments of informatics methods, including those that showcase the strengths and weaknesses of utilizing LLMs.
Theme: Improving LLMs with Consistency, Reliability, Explainability, and Safety
NeuroSymbolic and Knowledge-infused Learning
We welcome original research papers in four types of submissions:
A skilled and multidisciplinary program committee will evaluate all submitted papers, focusing on the originality of the work and its relevance to the workshop's theme. Acceptance of papers will adhere to the KDD 2024 Conference Template and undergo a double-blind review process. More details regarding submission can also be found at https://kdd2024.kdd.org/research-track-call-for-papers/. Selected papers will be presented at the workshop and published as open-access in the workshop proceedings through CEUR, where they will be available as archival content.
University of Maryland Baltimore County, USA
(Primary Contact)
Email: manas@umbc.edu
Samsung Research, Cambridge, UK
Email: efi.tsamoura@samsung.com
Booz Allen Hamilton, USA
Email: Raff_Edward@bah.com
Amazon, USA
Email: veduln@amazon.com
Ohio State University, USA
Email: srini@cse.ohio-state.edu