KGGDG generates harder distractors for medical MCQs by walking a knowledge graph to find misleading paths and feeding them to an LLM, lowering LLM accuracy on most benchmarks tested.
Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs
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abstract
Human conversations naturally evolve around related concepts and scatter to multi-hop concepts. This paper presents a new conversation generation model, ConceptFlow, which leverages commonsense knowledge graphs to explicitly model conversation flows. By grounding conversations to the concept space, ConceptFlow represents the potential conversation flow as traverses in the concept space along commonsense relations. The traverse is guided by graph attentions in the concept graph, moving towards more meaningful directions in the concept space, in order to generate more semantic and informative responses. Experiments on Reddit conversations demonstrate ConceptFlow's effectiveness over previous knowledge-aware conversation models and GPT-2 based models while using 70% fewer parameters, confirming the advantage of explicit modeling conversation structures. All source codes of this work are available at https://github.com/thunlp/ConceptFlow.
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cs.CL 1years
2025 1verdicts
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Enhancing Clinical Multiple-Choice Questions Benchmarks with Knowledge Graph Guided Distractor Generation
KGGDG generates harder distractors for medical MCQs by walking a knowledge graph to find misleading paths and feeding them to an LLM, lowering LLM accuracy on most benchmarks tested.