Скачать или смотреть ReFT Explained

​In this session, Zhengxuan Wu and Aryaman Arora from Stanford present their paper, ReFT: Representation Finetuning for Language Models.


About the paper:
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Instead of adjusting model weights during fine-tuning, ReFT focuses on modifying the model’s internal representations to quickly fine-tune using up to 50 times less parameters than traditional PeFT methods

🔬 ReFT: Representation Finetuning for Language Models: https://arxiv.org/pdf/2404.03592

📝 Zhengxuan Wu, Aryaman Arora, Zheng Wang, Atticus Geiger, Dan Jurafsky, Christopher D. Manning, Christopher Potts.


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#ai #machinelearning #deeplearning #stanfordnlp #nlp