Question Answering with LLMs and Learning from Answer Sets
This addresses the need for automated symbolic reasoning in question answering, reducing reliance on human expertise, though it is incremental as it builds on existing methods like ILASP and ASP.
The paper tackled the problem of LLMs struggling with explicit commonsense reasoning in story-based question answering by introducing LLM2LAS, a hybrid system that automatically learns symbolic rules from examples, enabling correct answers to previously unseen questions.
Large Language Models (LLMs) excel at understanding natural language but struggle with explicit commonsense reasoning. A recent trend of research suggests that the combination of LLM with robust symbolic reasoning systems can overcome this problem on story-based question answering tasks. In this setting, existing approaches typically depend on human expertise to manually craft the symbolic component. We argue, however, that this component can also be automatically learned from examples. In this work, we introduce LLM2LAS, a hybrid system that effectively combines the natural language understanding capabilities of LLMs, the rule induction power of the Learning from Answer Sets (LAS) system ILASP, and the formal reasoning strengths of Answer Set Programming (ASP). LLMs are used to extract semantic structures from text, which ILASP then transforms into interpretable logic rules. These rules allow an ASP solver to perform precise and consistent reasoning, enabling correct answers to previously unseen questions. Empirical results outline the strengths and weaknesses of our automatic approach for learning and reasoning in a story-based question answering benchmark.