arXiv AI By JungMin Yun, YoungBin Kim

Beyond One-Shot Expansion: Contrastive Evidence Exploration for Multi-Hop Retrieval

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arXiv Computation and Language
Aug 31

PRISM: Agentic Retrieval with LLMs for Multi-Hop Question Answering

PRISM is an agentic retrieval framework that uses large language models in a structured loop to improve evidence gathering for multi‑hop question answering. It splits retrieval into three specialized agents—a Question Analyzer, a Selector focused on precision, and an Adder focused on recall—whose iterative interaction yields a compact yet comprehensive evidence set. Experiments on HotpotQA, 2WikiMultiHopQA, MuSiQue, and MultiHopRAG show that PRISM consistently outperforms strong baselines by achieving higher retrieval accuracy and filtering out distracting content.

By Md Mahadi Hasan Nahid, Davood Rafiei
arXiv Computation and Language
Oct 19

SAG2: Embedding-Free Multi-Hop Retrieval with BM25 and JEV

SAG2 is an embedding‑free multi‑hop retrieval system that relies on a single SQLite database, BM25, and contextual evidence decisions from JEV. It replaces dense indexes with a lexical index over original passages, allowing query‑time evidence selection without offline embedding computation or dedicated database servers. On several benchmark datasets, SAG2 achieves the highest Recall@5 and Recall@10, demonstrating strong retrieval accuracy with lightweight preparation and rapid indexing.

By Yuchao Wu