IAP uses RL to train LLMs to explicitly infer and apply implicit user intent in single-turn personalized QA, achieving ~7.5% average macro-score gains over baselines on LaMP-QA.
arXiv preprint arXiv:2402.02136 , year=
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Context-KG uses LLMs to extract user preferences and context from natural language, driving ontology-guided layouts and insights for knowledge graph visualization that improve interpretability and task performance over traditional force-directed methods.
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Training LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answering
IAP uses RL to train LLMs to explicitly infer and apply implicit user intent in single-turn personalized QA, achieving ~7.5% average macro-score gains over baselines on LaMP-QA.
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Context-KG: Context-Aware Knowledge Graph Visualization with User Preferences and Ontological Guidance
Context-KG uses LLMs to extract user preferences and context from natural language, driving ontology-guided layouts and insights for knowledge graph visualization that improve interpretability and task performance over traditional force-directed methods.