# LLM-based Discriminative Reasoning for Knowledge Graph Question Answering Source: [LLM-based Discriminative Reasoning for Knowledge Graph Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:48:55.522Z Authors: Mufan Xu, Kehai Chen, Xuefeng Bai, Muyun Yang, Tiejun Zhao, Min Zhang Published: 2024-12-17T08:07:16.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()