Buckets:
| license: mit | |
| task_categories: | |
| - text-generation | |
| language: | |
| - en | |
| size_categories: | |
| - 100K<n<1M | |
| This is a training data file for [Self-RAG](https://selfrag.github.io/) that generates outputs to diverse user queries as well as reflection tokens to call the retrieval system adaptively and criticize its own output and retrieved passages. | |
| Self-RAG is trained on our 150k diverse instruction-output pairs with interleaving passages and reflection tokens using the standard next-token prediction objective, enabling efficient and stable learning with fine-grained feedback. | |
| At inference, we leverage reflection tokens covering diverse aspects of generations to sample the best output aligning users' preferences. See full descriptions in [our paper](https://arxiv.org/abs/2310.11511) and [code](https://github.com/AkariAsai/self-rag). | |
| ## Citation and contact | |
| If you use this model, please cite our work: | |
| ``` | |
| @article{asai2023selfrag, | |
| author = {Asai, Akari and Wu, Zeqiu and Wang, Yizhong and Sil, Avirup and Hajishirzi, Hannaneh}, | |
| title = {{Self-RAG}: Learning to Retrieve, Generate, and Critique through Self-Reflection}, | |
| year = {2023}, | |
| journal = { arXiv preprint arXiv:2310.11511 }, | |
| URL = {https://arxiv.org/abs/2310.11511} | |
| } | |
| ``` |
Xet Storage Details
- Size:
- 1.27 kB
- Xet hash:
- 171436d6c3fe3847dcd33f2d0bc192e47c45266284d6187f8077d3fe9feb4a85
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