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pro vyhledávání: '"Malekar, Jinendra"'
The advent of 1-bit large language models (LLMs) has attracted considerable attention and opened up new research opportunities. However, 1-bit LLMs only improve a fraction of models by applying extreme quantization to the projection layers while leav
Externí odkaz:
http://arxiv.org/abs/2408.11939
Autor:
Mohammadi, Seyedali, Raff, Edward, Malekar, Jinendra, Palit, Vedant, Ferraro, Francis, Gaur, Manas
Publikováno v:
Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pages 364-388, November 2024, Miami, Florida, US. Association for Computational Linguistics
Language Models (LMs) are being proposed for mental health applications where the heightened risk of adverse outcomes means predictive performance may not be a sufficient litmus test of a model's utility in clinical practice. A model that can be trus
Externí odkaz:
http://arxiv.org/abs/2406.12058
Autor:
Roy, Kaushik, Zi, Yuxin, Gaur, Manas, Malekar, Jinendra, Zhang, Qi, Narayanan, Vignesh, Sheth, Amit
Language models have the potential to assess mental health using social media data. By analyzing online posts and conversations, these models can detect patterns indicating mental health conditions like depression, anxiety, or suicidal thoughts. They
Externí odkaz:
http://arxiv.org/abs/2306.09824
Autor:
Roy, Kaushik, Khandelwal, Vedant, Goswami, Raxit, Dolbir, Nathan, Malekar, Jinendra, Sheth, Amit
After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve: (a) Personalized patient unders
Externí odkaz:
http://arxiv.org/abs/2304.00025