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README.md

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-language:
-- en
-license: mit
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+---
+language:
+- en
+license: mit
+---
+
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+# <div align="center">🔥Toward General Instruction-Following Alignment for Retrieval-Augmented Generation<div>
+
+<p align="center">
+🤖️ <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>
+</p>
+
+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). 
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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 
+
+Please 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}, 
+}
+```