Papers by Xiaoxin He
FiDeLiS: Faithful Reasoning in Large Language Models for Knowledge Graph Question Answering (2025.findings-acl)
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| Challenge: | Existing retrieval-based or agent-based methods are prone to generating erroneous or hallucinated outputs. |
| Approach: | They propose a framework to leverage knowledge graphs as external knowledge sources to improve the factuality of LLM responses by anchoring answers to verifiable reasoning steps retrieved from KGs. |
| Outcome: | The proposed framework improves factuality and interpretability across benchmarks and reduces computational costs. |