This is a training data file for Self-RAG 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 and code.

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}
}