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- LLM
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This model has been finetuned following the approach described in the paper **BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
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This model has 410M parameters. See the paper [link](https://arxiv.org/abs/2404.18443) for details.
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## Citation
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If you find this repository helpful, please kindly consider citing the corresponding paper. Thanks!
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```
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@
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title={BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers},
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author={Ran Xu and Wenqi Shi and Yue Yu and Yuchen Zhuang and Yanqiao Zhu and May D. Wang and Joyce C. Ho and Chao Zhang and Carl Yang},
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year={2024},
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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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- LLM
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This model has been finetuned following the approach described in the paper **BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers**, published in EMNLP 2024. The associated GitHub repository is available here https://github.com/ritaranx/BMRetriever.
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This model has 410M parameters. See the paper [link](https://arxiv.org/abs/2404.18443) for details.
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## Citation
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If you find this repository helpful, please kindly consider citing the corresponding paper. Thanks!
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```
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@inproceedings{xu2024bmretriever,
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title={BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers},
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author={Ran Xu and Wenqi Shi and Yue Yu and Yuchen Zhuang and Yanqiao Zhu and May D. Wang and Joyce C. Ho and Chao Zhang and Carl Yang},
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year={2024},
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booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
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}
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```
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