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language:
  - en
license: mit

🔥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}, 
}