将外部文本拆解为原子事实，提升有限上下文下的问答效率。
Context-Efficient Retrieval with Factual Decomposition
- 将外部文本预处理为半结构化原子事实，优化检索路径。
- 在限制召回文本量时，显著提升多个问答任务性能。
- 适合需要高效推理与低上下文开销的应用场景。
近期，将信息检索融入大语言模型（LLMs）受到广泛关注。从动态扩展的外部文本语料库中检索信息，使模型能融入最新事件，可视为一种情景记忆。本文证明，将外部语料库预处理为半结构化的“原子事实”，能提升检索效率。具体而言，这种原子事实形式在召回文本量受限时，显著提升了多个问答任务的表现。限制召回内容数量可缩小上下文规模，从而提升推理效率。
原文摘要 · Abstract (English)
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.
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