我要投稿
微信搜一搜，关注“NLP前沿”
FlashRAG 是一个用于再现和开发检索增强生成 (RAG) 研究的 Python 工具包。工具包包括 32 个预处理的基准 RAG 数据集和 12 个最先进的 RAG 算法。
https://github.com/RUC-NLPIR/FlashRAG
https://arxiv.org/html/2405.13576v1
框架特点：
内置的先进的RAG算法及评测效果表：
| Method | Type | NQ (EM) | TriviaQA (EM) | Hotpotqa (F1) | 2Wiki (F1) | PopQA (F1) | WebQA(EM) | Specific setting |
|---|---|---|---|---|---|---|---|---|
| Naive Generation | Sequential | 22.6 | 55.7 | 28.4 | 33.9 | 21.7 | 18.8 | |
| Standard RAG | Sequential | 35.1 | 58.9 | 35.3 | 21.0 | 36.7 | 15.7 | |
| AAR-contriever-kilt | Sequential | 30.1 | 56.8 | 33.4 | 19.8 | 36.1 | 16.1 | |
| LongLLMLingua | Sequential | 32.2 | 59.2 | 37.5 | 25.0 | 38.7 | 17.5 | Compress Ratio=0.5 |
| RECOMP-abstractive | Sequential | 33.1 | 56.4 | 37.5 | 32.4 | 39.9 | 20.2 | |
| Selective-Context | Sequential | 30.5 | 55.6 | 34.4 | 18.5 | 33.5 | 17.3 | Compress Ratio=0.5 |
| Ret-Robust | Sequential | 42.9 | 68.2 | 35.8 | 43.4 | 57.2 | 33.7 | Use LLAMA2-13B with trained lora |
| SuRe | Branching | 37.1 | 53.2 | 33.4 | 20.6 | 48.1 | 24.2 | Use provided prompt |
| REPLUG | Branching | 28.9 | 57.7 | 31.2 | 21.1 | 27.8 | 20.2 | |
| SKR | Conditional | 25.5 | 55.9 | 29.8 | 28.5 | 24.5 | 18.6 | Use infernece-time training data |
| Self-RAG | Loop | 36.4 | 38.2 | 29.6 | 25.1 | 32.7 | 21.9 | Use trained selfrag-llama2-7B |
| FLARE | Loop | 22.5 | 55.8 | 28.0 | 33.9 | 20.7 | 20.2 | |
| Iter-Retgen, ITRG | Loop | 36.8 | 60.1 | 38.3 | 21.6 | 37.9 | 18.2 |
53AI，企业落地大模型首选服务商
产品：场景落地咨询+大模型应用平台+行业解决方案
承诺：免费场景POC验证，效果验证后签署服务协议。零风险落地应用大模型，已交付160+中大型企业
2025-04-29
2025-04-30
2025-04-20
2025-04-29
2025-05-08
2025-04-20
2025-05-21
2025-04-26
2025-04-20
2025-04-29
回到顶部
粤ICP备17114055号
