Context-Efficient Retrieval with Factual Decomposition

Fuente: arXiv
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Main Authors: Li, Yanhong, Yunis, David, McAllester, David, Zhou, Jiawei
Format: Preprint
Published: 2025
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author Li, Yanhong
Yunis, David
McAllester, David
Zhou, Jiawei
author_facet Li, Yanhong
Yunis, David
McAllester, David
Zhou, Jiawei
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2503_19574
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Context-Efficient Retrieval with Factual Decomposition
Li, Yanhong
Yunis, David
McAllester, David
Zhou, Jiawei
Computation and Language
Information Retrieval
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.
title Context-Efficient Retrieval with Factual Decomposition
topic Computation and Language
Information Retrieval
url https://arxiv.org/abs/2503.19574