Zobrazeno 1 - 10
of 432
pro vyhledávání: '"Gao Yanjun"'
Autor:
Gao, Yanjun, Myers, Skatje, Chen, Shan, Dligach, Dmitriy, Miller, Timothy A, Bitterman, Danielle, Chen, Guanhua, Mayampurath, Anoop, Churpek, Matthew, Afshar, Majid
Large language models (LLMs) are being explored for diagnostic decision support, yet their ability to estimate pre-test probabilities, vital for clinical decision-making, remains limited. This study evaluates two LLMs, Mistral-7B and Llama3-70B, usin
Externí odkaz:
http://arxiv.org/abs/2411.04962
Natural Language Processing (NLP) techniques have been increasingly integrated into clinical projects to advance clinical decision-making and improve patient outcomes. Such projects benefit from interdisciplinary team collaborations. This paper explo
Externí odkaz:
http://arxiv.org/abs/2410.00174
Autor:
Croxford, Emma, Gao, Yanjun, Pellegrino, Nicholas, Wong, Karen K., Wills, Graham, First, Elliot, Liao, Frank J., Goswami, Cherodeep, Patterson, Brian, Afshar, Majid
Large Language Models have advanced clinical Natural Language Generation, creating opportunities to manage the volume of medical text. However, the high-stakes nature of medicine requires reliable evaluation, which remains a challenge. In this narrat
Externí odkaz:
http://arxiv.org/abs/2409.18170
Autor:
Myers, Skatje, Miller, Timothy A., Gao, Yanjun, Churpek, Matthew M., Mayampurath, Anoop, Dligach, Dmitriy, Afshar, Majid
Objective: Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text sou
Externí odkaz:
http://arxiv.org/abs/2409.15163
Autor:
Gao, Yanjun, Myers, Skatje, Chen, Shan, Dligach, Dmitriy, Miller, Timothy A, Bitterman, Danielle, Churpek, Matthew, Afshar, Majid
The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Despite these advancements, integrating tabular data, especially numerical
Externí odkaz:
http://arxiv.org/abs/2408.11854
Autor:
Li, Ruizhe, Gao, Yanjun
Large Language Models (LLMs), such as the GPT-4 and LLaMA families, have demonstrated considerable success across diverse tasks, including multiple-choice questions (MCQs). However, these models exhibit a positional bias, particularly an even worse a
Externí odkaz:
http://arxiv.org/abs/2405.03205
Autor:
Zhao, Jingjing, Zhang, Yizhu, Gao, Yanjun, Li, Meng, Liu, Xiaokun, Liu, Weimin, Yan, Tian-Min, Jiang, Yuhai
Mixing the fundamental ($\omega$) and the second harmonic (2$\omega$) waves in gas phase is a widely employed technique for emitting terahertz (THz) pulses. The THz generation driven by bi-chromatic fields can be described by the photocurrent model,
Externí odkaz:
http://arxiv.org/abs/2404.08876
Autor:
Chen, Shan, Gallifant, Jack, Guevara, Marco, Gao, Yanjun, Afshar, Majid, Miller, Timothy, Dligach, Dmitriy, Bitterman, Danielle S.
Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synthetic data generated from advanced language models. Promising results s
Externí odkaz:
http://arxiv.org/abs/2403.19511
Knowledge distillation, the technique of transferring knowledge from large, complex models to smaller ones, marks a pivotal step towards efficient AI deployment. Distilling Step-by-Step~(DSS), a novel method utilizing chain-of-thought~(CoT) distillat
Externí odkaz:
http://arxiv.org/abs/2403.03348
Publikováno v:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2024
Deep learning-based methods have demonstrated encouraging results in tackling the task of panoramic image inpainting. However, it is challenging for existing methods to distinguish valid pixels from invalid pixels and find suitable references for cor
Externí odkaz:
http://arxiv.org/abs/2402.02936