metadata
license: apache-2.0
⚡ Introduction
RAG-Instruct! 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 |