---
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}, 
}
```