NAACL 2025short0 citations
Context-Efficient Retrieval with Factual Decomposition
Yanhong Li, David Yunis, David McAllester, Jiawei Zhou
Abstract
There has recently been considerable interest in incorporating information retrieval into large language models (LLMs). Retrieval from a dynamically expanding external corpus of text allows a model to incorporate current events and can be viewed as a form of episodic memory. Here we demonstrate that pre-processing the external corpus into semi-structured “atomic facts” makes retrieval more efficient. More specifically, we demonstrate that our particular form of atomic facts improves performance on various question answering tasks when the amount of retrieved text is limited. Limiting the amount of retrieval reduces the size of the context and improves inference efficiency.
BibTeX
@inproceedings{li-etal-2025-context,
title = "Context-Efficient Retrieval with Factual Decomposition",
author = "Li, Yanhong and
Yunis, David and
McAllester, David and
Zhou, Jiawei",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-short.16/",
pages = "178--194",
ISBN = "979-8-89176-190-2"
}