Context-dependent neuro-symbolic AI through self-supervised learning with large language models

This study proposes methods to enable neuro-symbolic artificial intelligence (AI) to learn context dependency through self-supervised learning with large language models (LLMs). While neuro-symbolic AI can utilize extensive and ambiguous knowledge bases to prove theorems, LLMs encounter significant challenges in this domain. However, conventional neuro-symbolic AI faces practical issues, as the search space for reasoning expands rapidly with the increasing size of the knowledge base. Therefore, this study utilizes self-supervised learning with LLMs to incorporate context dependency into neuro-symbolic AI, thereby reducing the search space for reasoning. Existing approaches cannot achieve this because they fail to learn context dependency. The proposed network is the first to combine neuro-symbolic AI and LLMs to reduce the search space for reasoning. The authors trained and evaluated the proposed network using a novel reasoning dataset with context and developed an evaluation metric to validate the effectiveness of search space reduction. Results demonstrate that the proposed network significantly reduces the search space while maintaining accuracy compared to existing baseline methods. This network provides a solution to the frame problem and has potential applications in laboratory automation and autonomous robotics, where reasoning with a large search space is required. • Introducing a novel network utilizing neuro-symbolic AI that significantly reduces the reasoning search space by integrating context-dependent human-like reasoning. • Proposing a self-supervised learning framework combining LLMs and neuro-symbolic AI, pioneering a new cross-disciplinary field. • Developing an innovative approach for constructing reasoning datasets that incorporate context. • Establishing an evaluation metric to assess the efficacy of search space reduction in reasoning.