Update README.md (#2)

- Update README.md (c1765add6d9ad3aeded63c40348533f8bc477fc7)


Co-authored-by: Wanlong Liu <[email protected]>

README.md

@@ -1,3 +1,40 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- FreedomIntelligence/RAG-Instruct
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- meta-llama/Llama-3.2-3B
+pipeline_tag: text-generation
+---
+
+## ⚡ Introduction
+
+[RAG-Instruct](https://arxiv.org/abs/2501.00353) is a method for generating diverse and high-quality RAG instruction data. It synthesizes instruction datasets based on any source corpus, leveraging the following approaches:
+
+- **Five RAG paradigms**, which represent diverse query-document relationships to enhance model generalization across tasks.
+- **Instruction simulation**, which enriches instruction diversity and quality by utilizing the strengths of existing instruction datasets.
+
+Using this approach, we constructed a 40K instruction dataset from Wikipedia, covering a wide range of RAG scenarios and tasks. 
+Our RAG-Instruct significantly enhances the RAG ability of LLMs, demonstrating remarkable improvements in RAG performance across various tasks.
+
+| Model                          | WQA (acc) | PQA (acc) | TQA (acc) | OBQA (EM) | Pub (EM) | ARC (EM) | 2WIKI (acc) | HotP (acc) | MSQ (acc) | CFQA (EM) | PubMed (EM) |
+|--------------------------------|-----------|-----------|-----------|-----------|----------|----------|-------------|------------|-----------|-----------|-------------|
+| Llama3.2-3B                    | 58.7 | 61.8 | 69.7 |  77.0 | 55.0 | 66.8 | 55.6 | 40.2 | 13.2 | 46.8 | 70.3 |
+| Llama3.2-3B + **RAG-Instruct**     | 65.3                      | 64.0                | 77.0               |  81.2                           | 66.4                    | 73.0                    | 72.9                 | 52.7           | 25.0            | 50.3                     | 72.6                    | 
+
+## 📖 Citation
+```
+@misc{liu2024raginstructboostingllmsdiverse,
+      title={RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions}, 
+      author={Wanlong Liu and Junying Chen and Ke Ji and Li Zhou and Wenyu Chen and Benyou Wang},
+      year={2024},
+      eprint={2501.00353},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2501.00353}, 
+}
+```