Toward General Instruction-Following Alignment for Retrieval-Augmented Generation (2410.09584v1)

Abstract: Following natural instructions is crucial for the effective application of Retrieval-Augmented Generation (RAG) systems. Despite recent advancements in 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.

• The paper introduces VIF-RAG as a novel framework that automates the synthesis and verification of complex instructions, significantly improving instruction-following alignment in RAG systems.
• The methodology combines manual and automated processes to generate over 100,000 high-quality instruction-query pairs while ensuring semantic coherence via dual verification.
• Experiments on the FollowRAG benchmark demonstrate enhanced LLM performance in complex retrieval-augmented scenarios, highlighting the framework's practical scalability and robustness.

Toward General Instruction-Following Alignment for Retrieval-Augmented Generation

The paper introduces VIF-RAG, a novel framework aimed at improving instruction-following (IF) alignment in Retrieval-Augmented Generation (RAG) systems. Despite the progress in LLMs, there remains a significant gap in the effective assessment and enhancement of IF capabilities when integrated with RAG. This framework offers a comprehensive approach to synthesizing high-quality instruction data and automating its verification, thereby addressing challenges in complex RAG environments.

Overview of VIF-RAG Framework

VIF-RAG is proposed as the first automated, scalable, and verifiable instruction synthesis pipeline specifically designed for the RAG context. This framework innovatively combines manual and automated processes to create complex instruction datasets from a minimal initial set of atomic instructions. The steps of VIF-RAG are summarized as follows:

1. Instruction Synthesis: The framework begins by manually crafting less than 100 atomic instructions across four constraint types: format, semantics, knowledge, and lexical. It then utilizes composition rules to create complex instruction combinations, incorporating verification for semantic coherence.
2. Verification and Rewriting: A supervised model aids in instruction rewriting, generating variations and verifying quality through an executor-based system. This method ensures that each augmented instruction maintains high integrity and adheres to specified constraints.
3. Instruction-Query Integration: VIF-RAG synthesizes instruction-query pairs by integrating the instructions with queries from both RAG and general domains, creating a dataset (VIF-RAG-QA) with over 100,000 high-quality samples.
4. Dual Verification: Finally, dual verification filters out inconsistencies and checks execution quality, ensuring both the instructions and their integration with queries are flawless.

FollowRAG Benchmark

The paper also introduces FollowRAG, a benchmark explicitly designed to evaluate IF capabilities in RAG systems. It includes approximately 3,000 samples, assessing a range of 22 instruction constraints. Notably, FollowRAG is engineered to fit seamlessly with existing RAG benchmarks, offering a comprehensive evaluation suite.

Key Findings and Implications

The experiments on FollowRAG demonstrate that VIF-RAG significantly enhances LLM performance in handling RAG scenarios. Specifically, the synthesis pipeline and benchmark highlight the framework's robustness in aligning LLM capabilities with complex, multi-instruction constraints.

1. Instruction Understanding: VIF-RAG shows substantial improvements in instruction-following capabilities without sacrificing RAG performance, achieving better alignment than baseline models.
2. Scalability: The framework's ability to expand from a minimal instruction set while ensuring quality through dual verification makes it highly scalable and adaptable across different models and scenarios.
3. Practical Implications: VIF-RAG's advancements are particularly applicable in real-world scenarios where user interactions are not limited to standard templates. The framework effectively manages diverse and complex instructions, crucial for practical AI deployments.
4. Future Directions: The paper suggests focusing on enhancing scalability further and exploring additional types of instructions to broaden the application spectrum of RAG systems.

In conclusion, VIF-RAG represents a significant step toward aligning RAG systems with the dynamic and varied requirements of practical instruction-following tasks. The introduction of automated validation processes and comprehensive benchmark testing positions this work as a foundational effort in advancing the intersection of LLMs and RAG. Future research may expand on these principles to further refine and optimize interaction strategies in increasingly complex information environments.