Toward General Instruction-Following Alignment for Retrieval-Augmented
  Generation
Authors: name: Guanting Dong, name: Xiaoshuai Song, name: Yutao Zhu, name: Runqi Qiao, name: Zhicheng Dou, name: Ji-Rong Wen

Following natural instructions is crucial for the effective application of
Retrieval-Augmented Generation (RAG) systems. Despite recent advancements in
Large Language Models (LLMs), research on assessing and improving
instruction-following (IF) alignment within the RAG domain remains limited. To
address this issue, we propose VIF-RAG, the first automated, scalable, and
verifiable synthetic pipeline for instruction-following alignment in RAG
systems. We start by manually crafting a minimal set of atomic instructions
(<100) and developing combination rules to synthesize and verify complex
instructions for a seed set. We then use supervised models for instruction
rewriting while simultaneously generating code to automate the verification of
instruction quality via a Python executor. Finally, we integrate these
instructions with extensive RAG and general data samples, scaling up to a
high-quality VIF-RAG-QA dataset (>100k) through automated processes. To further
bridge the gap in instruction-following auto-evaluation for RAG systems, we
introduce FollowRAG Benchmark, which includes approximately 3K test samples,
covering 22 categories of general instruction constraints and four
knowledge-intensive QA datasets. Due to its robust pipeline design, FollowRAG
can seamlessly integrate with different RAG benchmarks. Using FollowRAG and
eight widely-used IF and foundational abilities benchmarks for LLMs, we
demonstrate that VIF-RAG markedly enhances LLM performance across a broad range
of general instruction constraints while effectively leveraging its
capabilities in RAG scenarios. Further analysis offers practical insights for
achieving IF alignment in RAG systems. Our code and datasets are released at
https://FollowRAG.github.io.