Multi-hop question answering (QA) requires models to retrieve and reason over multiple pieces of evidence. While Retrieval-Augmented Generation (RAG) has made progress in this area, existing methods often suffer from two key limitations: (1) fixed or overly frequent retrieval steps, and (2) ineffective use of previously retrieved knowledge. We propose MIND (Memory-Informed and INteractive Dynamic RAG), a framework that addresses these challenges through: (i) prompt-based entity extraction to identify reasoning-relevant elements, (ii) dynamic retrieval triggering based on token-level entropy and attention signals, and (iii) memory-aware filtering, which stores high-confidence facts across reasoning steps to enable consistent multi-hop generation.
@article{arxiv.2503.23095,
title = {Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering},
author = {Yuelyu Ji and Rui Meng and Zhuochun Li and Daqing He},
journal= {arXiv preprint arXiv:2503.23095},
year = {2025}
}