Retrieval-augmented generation (RAG) lets a large language model (LLM) answer questions by retrieving relevant passages from a large corpus. However, RAG struggles with multi-hop questions, which requ
DeFT-RAG improves multi-hop question answering by generating self-contained sub-questions sequentially, offloading reasoning to rule-based updates and a lightweight decision model, and caching answers to reduce LLM usage.
Retrieval-augmented generation (RAG) lets a large language model (LLM) answer questions by retrieving relevant passages from a large corpus. However, RAG struggles with multi-hop questions, which require chaining facts spread across several documents through intermediate entities that the question never mentions. Graph-based RAG methods retrieve once from the entities a question names and so rarely recover the full evidence chain. Iterative RAG methods recover it step by step, but their accuracy is limited by the many tasks they ask the LLM to perform, such as planning the sub-questions, deciding their dependencies, rewriting them as answers arrive, and keeping track of what is known. We…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
DeFT-RAG: Decomposing Multi-Hop Questions into Self-Contained Sub-Questions with Reusable Answers was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 11 checks on 2 different days. Its best position was #20 of 30, reached on Nov 3, 15:28 UTC. It dropped off the list after Nov 4, 01:28 UTC.
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#20 on Hugging Face · Daily papers, on Nov 3, 15:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 3, 2026 and has been on the list on 2 days (11 checks) since.