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dongguanting
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README.md
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license: mit
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language:
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- en
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license: mit
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---
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# <div align="center">🔥Toward General Instruction-Following Alignment for Retrieval-Augmented Generation<div>
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<p align="center">
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🤖️ <a href="https://followrag.github.io/" target="_blank">Website</a> • 🤗 <a href="https://huggingface.co/datasets/dongguanting/VIF-RAG-QA-110K" target="_blank">VIF-RAG-QA-110K</a> • 👉 <a href="https://huggingface.co/datasets/dongguanting/VIF-RAG-QA-20K" target="_blank">VIF-RAG-QA-20K</a> • 📖 <a href="https://arxiv.org/abs/2410.09584" target="_blank">Arxiv</a> • 🤗 <a href="https://huggingface.co/papers/2410.09584" target="_blank">HF-Paper</a> <br>
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</p>
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We propose a instruction-following alignement pipline named **VIF-RAG framework** and auto-evaluation Benchmark named **FollowRAG**:
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- **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).
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- **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
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## 🎖 Citation
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Please cite our work if you find the repository helpful.
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```
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@misc{dong2024general,
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title={Toward General Instruction-Following Alignment for Retrieval-Augmented Generation},
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author={Guanting Dong and Xiaoshuai Song and Yutao Zhu and Runqi Qiao and Zhicheng Dou and Ji-Rong Wen},
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year={2024},
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eprint={2410.09584},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2410.09584},
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}
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```
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