Zobrazeno 1 - 10
of 3 627
pro vyhledávání: '"Elangovan P"'
Autor:
Ke, Yu He, Jin, Liyuan, Elangovan, Kabilan, Abdullah, Hairil Rizal, Liu, Nan, Sia, Alex Tiong Heng, Soh, Chai Rick, Tung, Joshua Yi Min, Ong, Jasmine Chiat Ling, Kuo, Chang-Fu, Wu, Shao-Chun, Kovacheva, Vesela P., Ting, Daniel Shu Wei
Large Language Models (LLMs) show potential for medical applications but often lack specialized clinical knowledge. Retrieval Augmented Generation (RAG) allows customization with domain-specific information, making it suitable for healthcare. This st
Externí odkaz:
http://arxiv.org/abs/2410.08431
Large language models (LLMs) can generate fluent summaries across domains using prompting techniques, reducing the need to train models for summarization applications. However, crafting effective prompts that guide LLMs to generate summaries with the
Externí odkaz:
http://arxiv.org/abs/2410.02741
Autor:
Elangovan, Aparna, Ko, Jongwoo, Xu, Lei, Elyasi, Mahsa, Liu, Ling, Bodapati, Sravan, Roth, Dan
The effectiveness of automatic evaluation of generative models is typically measured by comparing it to human evaluation using correlation metrics. However, metrics like Krippendorff's $\alpha$ and Randolph's $\kappa$, originally designed to measure
Externí odkaz:
http://arxiv.org/abs/2410.03775
Autor:
Thakuria, Niharika, Malhotra, Akul, Thirumala, Sandeep K., Elangovan, Reena, Raghunathan, Anand, Gupta, Sumeet K.
Ternary Deep Neural Networks (DNN) have shown a large potential for highly energy-constrained systems by virtue of their low power operation (due to ultra-low precision) with only a mild degradation in accuracy. To enable an energy-efficient hardware
Externí odkaz:
http://arxiv.org/abs/2408.13617
Autor:
Tan, Ting Fang, Elangovan, Kabilan, Ong, Jasmine, Shah, Nigam, Sung, Joseph, Wong, Tien Yin, Xue, Lan, Liu, Nan, Wang, Haibo, Kuo, Chang Fu, Chesterman, Simon, Yeong, Zee Kin, Ting, Daniel SW
A comprehensive qualitative evaluation framework for large language models (LLM) in healthcare that expands beyond traditional accuracy and quantitative metrics needed. We propose 5 key aspects for evaluation of LLMs: Safety, Consensus, Objectivity,
Externí odkaz:
http://arxiv.org/abs/2407.07666
Autor:
Elangovan, Kabilan, Ong, Jasmine Chiat Ling, Jin, Liyuan, Seng, Benjamin Jun Jie, Kwan, Yu Heng, Tan, Lit Soo, Zhong, Ryan Jian, Ma, Justina Koi Li, Ke, YuHe, Liu, Nan, Giacomini, Kathleen M, Ting, Daniel Shu Wei
Large Language Models (LLMs) have emerged as a potential solution to assist digital health development with patient education, commonly medication-related enquires. We trained and validated Med-Pal, a medication domain-specific LLM-chatbot fine-tuned
Externí odkaz:
http://arxiv.org/abs/2407.12822
In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon insights from disciplines such as user experience research and human behavioral psychology to
Externí odkaz:
http://arxiv.org/abs/2405.18638
Autor:
Squires, Matthew, Tao, Xiaohui, Elangovan, Soman, Acharya, U Rajendra, Gururajan, Raj, Xie, Haoran, Zhou, Xujuan
Suicide is a prominent issue in society. Unfortunately, many people at risk for suicide do not receive the support required. Barriers to people receiving support include social stigma and lack of access to mental health care. With the popularity of s
Externí odkaz:
http://arxiv.org/abs/2405.05795
Autor:
Tan, Shawn Zheng Kai, Baksi, Shounak, Bjerregaard, Thomas Gade, Elangovan, Preethi, Gopalakrishnan, Thrishna Kuttikattu, Hric, Darko, Joumaa, Joffrey, Li, Beidi, Rabbani, Kashif, Venkatesan, Santhosh Kannan, Valdez, Joshua Daniel, Kuriakose, Saritha Vettikunnel
Biomedical data is growing exponentially, and managing it is increasingly challenging. While Findable, Accessible, Interoperable and Reusable (FAIR) data principles provide guidance, their adoption has proven difficult, especially in larger enterpris
Externí odkaz:
http://arxiv.org/abs/2405.05413
Autor:
Squires, Matthew, Tao, Xiaohui, Elangovan, Soman, Gururajan, Raj, Xie, Haoran, Zhou, Xujuan, Li, Yuefeng, Acharya, U Rajendra
Repetitive Transcranial Magnetic Stimulation (rTMS) is a well-supported, evidence-based treatment for depression. However, patterns of response to this treatment are inconsistent. Emerging evidence suggests that artificial intelligence can predict rT
Externí odkaz:
http://arxiv.org/abs/2404.16913