In Retrieval-Augmented Generation (RAG), retrieval is not always helpful and
applying it to every instruction is sub-optimal. Therefore, determining whether
to retrieve is crucial for RAG, which is usually referred to as Active
Retrieval. However, existing active retrieval methods face two challenges: 1.
They usually rely on a single criterion, which struggles with handling various
types of instructions. 2. They depend on specialized and highly differentiated
procedures, and thus combining them makes the RAG system more complicated and
leads to higher response latency. To address these challenges, we propose
Unified Active Retrieval (UAR). UAR contains four orthogonal criteria and casts
them into plug-and-play classification tasks, which achieves multifaceted
retrieval timing judgements with negligible extra inference cost. We further
introduce the Unified Active Retrieval Criteria (UAR-Criteria), designed to
process diverse active retrieval scenarios through a standardized procedure.
Experiments on four representative types of user instructions show that UAR
significantly outperforms existing work on the retrieval timing judgement and
the performance of downstream tasks, which shows the effectiveness of UAR and
its helpfulness to downstream tasks.

通过统一的主动检索准则，提出了退化增强生成（Retrieval-Augmented Generation，RAG）中重要的主动检索方法，并演示了在四个用户指令类型上的显著性能提升及对下游任务的帮助。

检索增强生成的统一主动检索

Unified Active Retrieval for Retrieval Augmented Generation

Recently the retrieval-augmented generation (RAG) paradigm has raised much
attention for its potential in incorporating external knowledge into large
language models (LLMs) without further training. While widely explored in
natural language applications, its utilization in code generation remains
under-explored. In this paper, we introduce Active Retrieval in Knowledge Soup
(ARKS), an advanced strategy for generalizing large language models for code.
In contrast to relying on a single source, we construct a knowledge soup
integrating web search, documentation, execution feedback, and evolved code
snippets. We employ an active retrieval strategy that iteratively refines the
query and updates the knowledge soup. To assess the performance of ARKS, we
compile a new benchmark comprising realistic coding problems associated with
frequently updated libraries and long-tail programming languages. Experimental
results on ChatGPT and CodeLlama demonstrate a substantial improvement in the
average execution accuracy of ARKS on LLMs. The analysis confirms the
effectiveness of our proposed knowledge soup and active retrieval strategies,
offering rich insights into the construction of effective retrieval-augmented
code generation (RACG) pipelines. Our model, code, and data are available at
this https URL

在本文中，我们介绍了 Active Retrieval in Knowledge Soup（ARKS），这是一种用于代码生成的高级策略，通过积极的检索策略和知识汇集构建，实现了大型语言模型的泛化，对一般化 RACG（retrieval-augmented code generation）流程的构建提供了有益的见解。