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New PRISM framework uses LLM agents for precise multi-hop question answering

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced PRISM, a novel agentic retrieval framework designed to enhance multi-hop question answering by leveraging large language models. PRISM breaks down complex queries into sub-questions using a Question Analyzer, then employs a Selector agent to precisely identify relevant context and an Adder agent to incorporate any missing evidence. This iterative process aims to produce a compact yet comprehensive set of evidence, reducing noise and improving accuracy for downstream QA models. Experiments on benchmarks like HotpotQA and MuSiQue show PRISM outperforming existing baselines. AI

IMPACT Enhances retrieval accuracy for complex question-answering systems, potentially improving performance in applications requiring deep information synthesis.

RANK_REASON The cluster contains a research paper detailing a new framework for question answering. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PRISM framework uses LLM agents for precise multi-hop question answering

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The cluster contains a research paper detailing a new framework for question answering. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Md Mahadi Hasan Nahid, Davood Rafiei ·

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

    arXiv:2510.14278v2 Announce Type: replace Abstract: Retrieval plays a central role in multi-hop question answering (QA), where answering complex questions requires gathering multiple pieces of evidence. We propose PRISM, an agentic retrieval framework that leverages large languag…

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