Duplicate from selfrag/selfrag_train_data

Co-authored-by: Akari Asai <akariasai@users.noreply.huggingface.co>

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README.md

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+---
+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}
+}
+```

train.jsonl

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