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README.md
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@@ -34,8 +34,8 @@ You can train the model using [NeMo Aligner](https://github.com/NVIDIA/NeMo-Alig
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## References
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* [DPO method](https://arxiv.org/abs/2305.18290)
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* [HelpSteer](https://arxiv.org/abs/2311.09528)
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* [Llama 3: Open Foundation and Instruct Models](https://ai.meta.com/blog/meta-llama-3/) <br>
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* [Meta's Llama 3 Webpage](https://llama.meta.com/llama3/) <br>
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* [Meta's Llama 3 Model Card](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) <br>
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If you find this model useful, please cite the following works
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```bibtex
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@misc{
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title={
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author={Zhilin Wang and Yi Dong and
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year={
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eprint={
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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## References
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* [HelpSteer2](https://arxiv.org/abs/2406.08673)
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* [DPO method](https://arxiv.org/abs/2305.18290)
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* [Llama 3: Open Foundation and Instruct Models](https://ai.meta.com/blog/meta-llama-3/) <br>
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* [Meta's Llama 3 Webpage](https://llama.meta.com/llama3/) <br>
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* [Meta's Llama 3 Model Card](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) <br>
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If you find this model useful, please cite the following works
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```bibtex
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@misc{wang2024helpsteer2,
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title={HelpSteer2: Open-source dataset for training top-performing reward models},
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author={Zhilin Wang and Yi Dong and Olivier Delalleau and Jiaqi Zeng and Gerald Shen and Daniel Egert and Jimmy J. Zhang and Makesh Narsimhan Sreedhar and Oleksii Kuchaiev},
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year={2024},
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eprint={2406.08673},
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archivePrefix={arXiv},
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primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
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}
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```
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