Enterprise retrieval augmented generation (RAG) offers a highly flexible
framework for combining powerful large language models (LLMs) with internal,
possibly temporally changing, documents. In RAG, documents are first chunked.
Relevant chunks are then retrieved for a specific user query, which are passed
as context to a synthesizer LLM to generate the query response. However, the
retrieval step can limit performance, as incorrect chunks can lead the
synthesizer LLM to generate a false response. This work proposes a zero-shot
adaptation of standard dense retrieval steps for more accurate chunk recall.
Specifically, a chunk is first decomposed into atomic statements. A set of
synthetic questions are then generated on these atoms (with the chunk as the
context). Dense retrieval involves finding the closest set of synthetic
questions, and associated chunks, to the user query. It is found that retrieval
with the atoms leads to higher recall than retrieval with chunks. Further
performance gain is observed with retrieval using the synthetic questions
generated over the atoms. Higher recall at the retrieval step enables higher
performance of the enterprise LLM using the RAG pipeline.

通过将文档切分成原子语句并生成与原子语句相关的合成问题，本研究实现了在企业级检索增强生成中更准确的检索召回，并观察到使用原子语句检索和合成问题检索能够提高检索步骤的召回率，从而提高企业级大型语言模型的性能。

基于原子单元的企业 RAG 的问题检索

Question-Based Retrieval using Atomic Units for Enterprise RAG

Synthesizing QA pairs with a question generator (QG) on the target domain has
become a popular approach for domain adaptation of question answering (QA)
models. Since synthetic questions are often noisy in practice, existing work
adapts scores from a pretrained QA (or QG) model as criteria to select
high-quality questions. However, these scores do not directly serve the
ultimate goal of improving QA performance on the target domain. In this paper,
we introduce a novel idea of training a question value estimator (QVE) that
directly estimates the usefulness of synthetic questions for improving the
target-domain QA performance. By conducting comprehensive experiments, we show
that the synthetic questions selected by QVE can help achieve better
target-domain QA performance, in comparison with existing techniques. We
additionally show that by using such questions and only around 15% of the human
annotations on the target domain, we can achieve comparable performance to the
fully-supervised baselines.

本文提出了一种新颖的问题价值估计器（QVE），它可以直接估计合成问题对于提高目标领域问答（QA）性能的有用性。通过综合实验，我们发现 QVE 选择的合成问题可以帮助实现比现有技术更好的目标领域 QA 表现，并且通过使用这些问题并仅使用目标领域 15％左右的人类注释，我们可以实现与完全监督基线相当的性能。