EpiGraph creates a heterogeneous epilepsy knowledge graph that boosts LLM performance on clinical reasoning tasks by 30-41% in pharmacogenomics when used with Graph-RAG.
Retrieval-augmented generation for knowledge-intensive nlp tasks
4 Pith papers cite this work. Polarity classification is still indexing.
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ERL trains LLMs to erase faulty reasoning steps and regenerate them in place, yielding gains of up to 8.48% EM on multi-hop QA benchmarks like HotpotQA.
RioRAG uses nugget-centric verification with cross-source checks to create dense verifiable rewards for RL-based optimization of long-form RAG, yielding higher factual recall and faithfulness on LongFact and RAGChecker.
citing papers explorer
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EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild
EpiGraph creates a heterogeneous epilepsy knowledge graph that boosts LLM performance on clinical reasoning tasks by 30-41% in pharmacogenomics when used with Graph-RAG.
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Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs
ERL trains LLMs to erase faulty reasoning steps and regenerate them in place, yielding gains of up to 8.48% EM on multi-hop QA benchmarks like HotpotQA.
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Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation
RioRAG uses nugget-centric verification with cross-source checks to create dense verifiable rewards for RL-based optimization of long-form RAG, yielding higher factual recall and faithfulness on LongFact and RAGChecker.
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