We are thrilled to announce the Workshop on Neuro-Symbolic Learning and Reasoning in the Era of Large Language Models at {TBD} to be held on {TBD}. This workshop aims to provide a dedicated platform for researchers to present and share their cutting-edge advancements in the next generation of neuro-symbolic AI. By creating an environment conducive to knowledge exchange and the exploration of innovative ideas, we aim to foster collaboration and inspire new breakthroughs. We invite submissions on topics and questions related to neuro-symbolic AI, including but not limited to:

• Survey of recent NSAI methods for improving LLMs
• Neuro-symbolic approaches for learning and reasoning
• NSAI methods for high quality synthetic data generation and verification
• NSAI to help improve LLM judgements
• Code models, Programming languages, libraries and software tools for integration of various learning and reasoning formalisms
• Using probabilistic inference or logical inference in training deep models
• Deep learning and logical reasoning over structured and relational data
• Integration of non-differentiable optimization models in learning
• NSAI role in grounding LLMs, enhancing performance
• Specific aspects of neuro-symbolic modeling, such as data efficiency, scalability, and evaluation benchmarks
• Integration of expert knowledge in learning (e.g., fairness constraints, physics laws, FOL rules)
• Strategies to address challenges related to compositional generalization using NSAI
• We also welcome work on related topics (e.g. neural program synthesis, program induction, and concept learning)
• Datasets, evaluations, benchmarks, leaderboards

Important dates

• Submission deadline: TBD
• Acceptance notification: TBD
• Camera-ready deadline: TBD

Submission instructions

We invite 8-page full paper or 4-page short paper submissions. We also welcome 2-page extended abstracts for work that is published at {TBD} or other venues. All submissions should be anonymized, with the exception of extended abstracts of previously-published work, which do not need to be anonymized. The papers should be formatted according to {TBD} conference guidelines. Accepted papers will be non-archival. However, the PDFs will be hosted on the workshop website.