Promoting Knowledge Base Question Answering by Directing LLMs to Generate Task-relevant Logical Forms

Knowledge base question answering (KBQA) refers to the system that produces answers to user queries by reasoning with a large-scale structured knowledge base. Advanced works have achieved great success either by generating logical forms (LF) or directly generating answers. Although the former typically yields better performance, these generated LF could be inaccurate, e.g., non-executable. In this regard, large language models (LLMs) have shown exciting potential for accurate generation. However, it is challenging to fine-tune LLMs to generate LF. This is because the context retrieved for prediction typically leads to an excessive number of reasoning paths. In this context, LLMs can generate numerous LF corresponding to these reasoning paths, but a few LF can result in correct answers. Thus, fine-tuning LLMs to generate answer-relevant LF would conflict with the prior knowledge of the LLMs. In this work, we propose a novel learning framework, FM-KBQA, to fine-tune LLMs using multi-task learning for KBQA. Specifically, we propose to fine-tune LLMs using an additional objective: generating the index of reasoning paths that lead to correct answers. This will direct LLMs to pay attention to answer-relevant paths among numerous reasoning paths by completing a simple task where the selected reasoning paths can be supplementary for non-executable LF. Directly generating answers can make LLMs pay attention to the answer-relevant reasoning paths, but it is much more challenging than generating the index of reasoning paths. To verify FM-KBQA's effectiveness, we conduct experiments on mainstream benchmarks, such as WebQuestionsSP (WQSP) and ComplexWebQuestions (CWQ). Extensive evaluations across two public benchmark datasets underscore the superiority of FM-KBQA over current state-of-the-art methods.