🔥Toward General Instruction-Following Alignment for Retrieval-Augmented Generation

🤖️ Website • 🤗 VIF-RAG-QA-110K • 👉 VIF-RAG-QA-20K • 📖 Arxiv • 🤗 HF-Paper

We propose a instruction-following alignement pipline named **VIF-RAG framework** and auto-evaluation Benchmark named **FollowRAG**: - **IF-RAG:** It is the first automated, scalable, and verifiable data synthesis pipeline for aligning complex instruction-following in RAG scenarios. VIF-RAG integrates a verification process at each step of data augmentation and combination. We begin by manually creating a minimal set of atomic instructions (<100) and then apply steps including instruction composition, quality verification, instruction-query combination, and dual-stage verification to generate a large-scale, high-quality VIF-RAG-QA dataset (>100K). - **FollowRAG:** To address 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 4 knowledge-intensive QA datasets. Due to its robust pipeline design, FollowRAG can seamlessly integrate with different RAG benchmarks ## 🎖 Citation Please star our github repo and cite our work if you find the repository helpful. ``` @misc{dong2024general, title={Toward General Instruction-Following Alignment for Retrieval-Augmented Generation}, author={Guanting Dong and Xiaoshuai Song and Yutao Zhu and Runqi Qiao and Zhicheng Dou and Ji-Rong Wen}, year={2024}, eprint={2410.09584}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2410.09584}, } ```