Assessing and Enhancing Large Language Models in Rare Disease Question-answering

Autor: Wang, Guanchu, Ran, Junhao, Tang, Ruixiang, Chang, Chia-Yuan, Chuang, Yu-Neng, Liu, Zirui, Braverman, Vladimir, Liu, Zhandong, Hu, Xia

Despite the impressive capabilities of Large Language Models (LLMs) in general medical domains, questions remain about their performance in diagnosing rare diseases. To answer this question, we aim to assess the diagnostic performance of LLMs in rare

Externí odkaz: http://arxiv.org/abs/2408.08422

Understanding Different Design Choices in Training Large Time Series Models

Autor: Chuang, Yu-Neng, Li, Songchen, Yuan, Jiayi, Wang, Guanchu, Lai, Kwei-Herng, Yu, Leisheng, Ding, Sirui, Chang, Chia-Yuan, Tan, Qiaoyu, Zha, Daochen, Hu, Xia

Inspired by Large Language Models (LLMs), Time Series Forecasting (TSF), a long-standing task in time series analysis, is undergoing a transition towards Large Time Series Models (LTSMs), aiming to train universal transformer-based models for TSF. Ho

Externí odkaz: http://arxiv.org/abs/2406.14045

A manufacturable platform for photonic quantum computing

Autor: Alexander, Koen, Bahgat, Andrea, Benyamini, Avishai, Black, Dylan, Bonneau, Damien, Burgos, Stanley, Burridge, Ben, Campbell, Geoff, Catalano, Gabriel, Ceballos, Alex, Chang, Chia-Ming, Chung, CJ, Danesh, Fariba, Dauer, Tom, Davis, Michael, Dudley, Eric, Er-Xuan, Ping, Fargas, Josep, Farsi, Alessandro, Fenrich, Colleen, Frazer, Jonathan, Fukami, Masaya, Ganesan, Yogeeswaran, Gibson, Gary, Gimeno-Segovia, Mercedes, Goeldi, Sebastian, Goley, Patrick, Haislmaier, Ryan, Halimi, Sami, Hansen, Paul, Hardy, Sam, Horng, Jason, House, Matthew, Hu, Hong, Jadidi, Mehdi, Johansson, Henrik, Jones, Thomas, Kamineni, Vimal, Kelez, Nicholas, Koustuban, Ravi, Kovall, George, Krogen, Peter, Kumar, Nikhil, Liang, Yong, LiCausi, Nicholas, Llewellyn, Dan, Lokovic, Kimberly, Lovelady, Michael, Manfrinato, Vitor, Melnichuk, Ann, Souza, Mario, Mendoza, Gabriel, Moores, Brad, Mukherjee, Shaunak, Munns, Joseph, Musalem, Francois-Xavier, Najafi, Faraz, O'Brien, Jeremy L., Ortmann, J. Elliott, Pai, Sunil, Park, Bryan, Peng, Hsuan-Tung, Penthorn, Nicholas, Peterson, Brennan, Poush, Matt, Pryde, Geoff J., Ramprasad, Tarun, Ray, Gareth, Rodriguez, Angelita, Roxworthy, Brian, Rudolph, Terry, Saunders, Dylan J., Shadbolt, Pete, Shah, Deesha, Shin, Hyungki, Smith, Jake, Sohn, Ben, Sohn, Young-Ik, Son, Gyeongho, Sparrow, Chris, Staffaroni, Matteo, Stavrakas, Camille, Sukumaran, Vijay, Tamborini, Davide, Thompson, Mark G., Tran, Khanh, Triplet, Mark, Tung, Maryann, Vert, Alexey, Vidrighin, Mihai D., Vorobeichik, Ilya, Weigel, Peter, Wingert, Mathhew, Wooding, Jamie, Zhou, Xinran

Whilst holding great promise for low noise, ease of operation and networking, useful photonic quantum computing has been precluded by the need for beyond-state-of-the-art components, manufactured by the millions. Here we introduce a manufacturable pl

Externí odkaz: http://arxiv.org/abs/2404.17570

Beyond Self-Consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging

Autor: Chang, Chia-Hsuan, Lucas, Mary M., Lee, Yeawon, Yang, Christopher C., Lu-Yao, Grace

Advances in large language models (LLMs) have encouraged their adoption in the healthcare domain where vital clinical information is often contained in unstructured notes. Cancer staging status is available in clinical reports, but it requires natura

Externí odkaz: http://arxiv.org/abs/2404.13149

CanvasPic: An Interactive Tool for Freely Generating Facial Images Based on Spatial Layout

Autor: Wei, Jiafu, Chang, Chia-Ming, Yang, Xi, Igarashi, Takeo

In real-world usage, existing GAN image generation tools come up short due to their lack of intuitive interfaces and limited flexibility. To overcome these limitations, we developed CanvasPic, an innovative tool for flexible GAN image generation. Our

Externí odkaz: http://arxiv.org/abs/2404.10352