associated with 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024)
The aim of this workshop is to bring together researchers and practitioners working on the intersections of language models, knowledge representation, and reasoning, particularly, but not exclusively, in medical, law, and science domains. We encourage submissions that discuss novel techniques, approaches, and innovative ideas related to this topic. Topics of interest include, but are not limited to, the following:
Contributions may be regular papers (up to 9 pages) or position papers (up to 5 pages), including abstract, figures, and appendices (if any) but excluding references and acknowledgements. Submissions should follow the KR 2024 formatting guidelines and be submitted through the submission page. Each submission will be reviewed by at least two program committee members.
Reasoning is an essential component of human intelligence as it plays a fundamental role in our ability to think critically, support responsible decisions, and solve challenging problems. Traditionally, AI has addressed reasoning in the context of logic-based representations of knowledge. However, the recent leap forward in natural language processing, with the emergence of language models based on transformers, is hinting at the possibility that these models exhibit reasoning abilities, particularly as they grow in size and are trained on more data. Despite ongoing discussions about what reasoning is in language models, it is still not easy to pin down to what extent these models are actually capable of reasoning.
The goal of this workshop is to create a platform for researchers from different disciplines and/or AI perspectives, to explore approaches and techniques with the aim to reconcile reasoning between language models using transformers and using logic-based representations. The specific objectives include analyzing the reasoning abilities of language models measured alongside KR methods, injecting KR-style reasoning abilities into language models (including by neuro-symbolic means), and formalizing the kind of reasoning language models carry out. This exploration aims to uncover how language models can effectively integrate and leverage knowledge and reasoning with it, thus improving their application and utility in areas where precision and reliability are a key requirement.
Format: A combination of invited talks from different disciplines and/or AI perspectives, submitted papers with oral and poster presentations, and panel discussions. This format will foster a dynamic exchange of ideas, encourage in-depth dialogue among participants, and highlight the latest advancements in the field.
Expected activities: Invited talks from leading researchers, presentations of selected papers, poster sessions for additional papers and/or demonstrations, accompanied by interactive panel discussions to further promote networking and collaboration.
Target audience: Interdisciplinary researchers and professionals specializing in AI, natural language processing, and machine learning, with a special emphasis on individuals engaged in fields that require reasoning with knowledge, including medicine, law, and the sciences.
Research groups: Active research groups in the area of AI, natural language processing, knowledge representation and reasoning, and related fields.
Planned or confirmed invited speakers: To be determined.
Expected number of submissions and participants: Approximately 15-20 paper submissions and 50-100 participants.