🔥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. ``` @article{dong2024general, author = {Guanting Dong and Xiaoshuai Song and Yutao Zhu and Runqi Qiao and Zhicheng Dou and Ji{-}Rong Wen}, title = {Toward General Instruction-Following Alignment for Retrieval-Augmented Generation}, journal = {CoRR}, volume = {abs/2410.09584}, year = {2024}, url = {https://doi.org/10.48550/arXiv.2410.09584}, doi = {10.48550/ARXIV.2410.09584}, eprinttype = {arXiv}, eprint = {2410.09584}, timestamp = {Fri, 22 Nov 2024 21:38:25 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2410-09584.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }} ```