LLM-Based Discriminative Reasoning for Knowledge Graph Question Answering

ACL ARR 2025 February Submission1810 Authors

Abstract: Large language models (LLMs) based on generative pre-trained Transformer have achieved remarkable performance on knowledge graph question-answering (KGQA) tasks. However, LLMs often produce ungrounded subgraph planning or reasoning results in KGQA due to the hallucinatory behavior brought by the generative paradigm. To tackle this issue, we propose READS to reformulate the KGQA process into discriminative subtasks, which simplifies the search space for each subtasks. Based on the subtasks, we design a new corresponding discriminative inference strategy to conduct the reasoning for KGQA, thereby alleviating hallucination and ungrounded reasoning issues in LLMs. Experimental results show that the proposed approach outperforms multiple strong comparison methods, along with achieving state-of-the-art performance on widely used benchmarks WebQSP and CWQ.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: Question Anweing, knowledge base QA, reasoning

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 1810