File size: 2,744 Bytes
58fabea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
---
license: apache-2.0
datasets:
- FreedomIntelligence/RAG-Instruct
language:
- en
metrics:
- accuracy
base_model:
- meta-llama/Llama-3.1-8B
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 |
| Llama3.1-8B | 59.5 | 60.8 | 73.4 | 82.0 | 56.7 | 77.1 | 65.6 | 45.6 | 18.7 | 56.5 | 73.9 |
| 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 |
| Llama3.1-8B + **RAG-Instruct** | 69.7 | 68.4 | 79.3 | 84.8 | 77.2 | 79.9 | 79.3 | 56.4 | 30.3 | 57.8 | 77.0 |
## 📖 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},
}
``` |