STRIDE uses a meta-planner for entity-agnostic reasoning skeletons and a supervisor for dependency-aware execution to improve retrieval-augmented multi-hop QA.
In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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NanoKnow partitions QA questions by pre-training data presence to separate the effects of memorized facts from external evidence in LLM outputs.
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STRIDE: Strategic Iterative Decision-Making for Retrieval-Augmented Multi-Hop Question Answering
STRIDE uses a meta-planner for entity-agnostic reasoning skeletons and a supervisor for dependency-aware execution to improve retrieval-augmented multi-hop QA.