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While Large Language Models (LLMs) are being quickly adapted to many domains, including healthcare, their strengths and pitfalls remain under-explored. In our study, we examine the effects of prompt engineering to guide Large Language…

计算与语言 · 计算机科学 2024-09-04 Daniil Filienko , Yinzhou Wang , Caroline El Jazmi , Serena Xie , Trevor Cohen , Martine De Cock , Weichao Yuwen

Large language models (LLMs) are useful in many NLP tasks and become more capable with size, with the best open-source models having over 50 billion parameters. However, using these 50B+ models requires high-end hardware, making them…

This paper explores the potential of using Large Language Models (LLMs) to automate the evaluation of responses in medical Question and Answer (Q\&A) systems, a crucial form of Natural Language Processing. Traditionally, human evaluation…

计算与语言 · 计算机科学 2024-09-04 Jack Krolik , Herprit Mahal , Feroz Ahmad , Gaurav Trivedi , Bahador Saket

As AI becomes fundamental in sectors like healthcare, explainable AI (XAI) tools are essential for trust and transparency. However, traditional user studies used to evaluate these tools are often costly, time consuming, and difficult to…

Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis. To enable their use in clinical settings, LLMs are typically further adapted through continued pretraining…

Large Language Models (LLMs), such as GPT3.5, have exhibited remarkable proficiency in comprehending and generating natural language. On the other hand, medical assistants hold the potential to offer substantial benefits for individuals.…

计算与语言 · 计算机科学 2024-04-05 Kai Zhang , Yangyang Kang , Fubang Zhao , Xiaozhong Liu

Frontier large language models (LLMs), such as GPT-5, Claude 4.5, Gemini 3, Llama 4, and DeepSeek-R1, represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare by generating human-like responses…

This study applies Large Language Models (LLMs) to two foundational Electronic Health Record (EHR) data science tasks: structured data querying (using programmatic languages, Python/Pandas) and information extraction from unstructured…

计算与语言 · 计算机科学 2026-01-29 Juan Jose Rubio Jan , Jack Wu , Julia Ive

Recent years have seen important advances in the building of interpretable models, machine learning models that are designed to be easily understood by humans. In this work, we show that large language models (LLMs) are remarkably good at…

机器学习 · 计算机科学 2024-02-23 Sebastian Bordt , Ben Lengerich , Harsha Nori , Rich Caruana

Large Language Models (LLMs) have demonstrated impressive capabilities in role-playing scenarios, particularly in simulating domain-specific experts using tailored prompts. This ability enables LLMs to adopt the persona of individuals with…

人工智能 · 计算机科学 2025-01-14 Xinyao Ma , Rui Zhu , Zihao Wang , Jingwei Xiong , Qingyu Chen , Haixu Tang , L. Jean Camp , Lucila Ohno-Machado

This study evaluates the capacity of large language models (LLMs) to generate structured clinical consultation templates for electronic consultation. Using 145 expert-crafted templates developed and routinely used by Stanford's eConsult…

There is enormous enthusiasm and concerns in using large language models (LLMs) in healthcare, yet current assumptions are all based on general-purpose LLMs such as ChatGPT. This study develops a clinical generative LLM, GatorTronGPT, using…

While Large Language Models (LLMs) have exhibited remarkable emergent capabilities through extensive pre-training, they still face critical limitations in generalizing to specialized domains and handling diverse linguistic variations, known…

计算与语言 · 计算机科学 2025-05-28 Jinwu Hu , Zhitian Zhang , Guohao Chen , Xutao Wen , Chao Shuai , Wei Luo , Bin Xiao , Yuanqing Li , Mingkui Tan

Large language models (LLMs) have shown promise in translating model-based explanations into human-readable narratives. This study evaluates whether LLMs can serve as post-hoc explainability interfaces for credit risk models, focusing on…

风险管理 · 定量金融 2026-05-19 Wenxi Geng , Dingyuan Liu , Liya Li , Yiqing Wang

Large language models (LLMs) such as ChatGPT, Gemini, LlaMa, and Claude are trained on massive quantities of text parsed from the internet and have shown a remarkable ability to respond to complex prompts in a manner often indistinguishable…

光学 · 物理学 2025-04-01 Darui Lu , Yang Deng , Jordan M. Malof , Willie J. Padilla

Large Language Models (LLMs), typified by OpenAI's GPT, have marked a significant advancement in artificial intelligence. Trained on vast amounts of text data, LLMs are capable of understanding and generating human-like text across a…

人工智能 · 计算机科学 2024-10-29 Haochen Zhang , Yuyang Dong , Chuan Xiao , Masafumi Oyamada

Recent work exploring the capabilities of pre-trained large language models (LLMs) has demonstrated their ability to act as general pattern machines by completing complex token sequences representing a wide array of tasks, including…

计算机与社会 · 计算机科学 2024-03-25 Seyed Parsa Neshaei , Richard Lee Davis , Adam Hazimeh , Bojan Lazarevski , Pierre Dillenbourg , Tanja Käser

Large language models (LLMs) show promise for supporting clinicians in diagnostic communication by generating explanations and guidance for patients. Yet their ability to produce outputs that are both understandable and empathetic remains…

计算与语言 · 计算机科学 2025-11-04 Jianzhou Yao , Shunchang Liu , Guillaume Drui , Rikard Pettersson , Alessandro Blasimme , Sara Kijewski

Large Language Models (LLMs) have emerged as powerful tools in various research domains. This article examines their potential through a literature review and firsthand experimentation. While LLMs offer benefits like cost-effectiveness and…

人机交互 · 计算机科学 2024-04-10 M. Namvarpour , A. Razi

Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks. However, their performance in high-performance computing (HPC) domain tasks has…

分布式、并行与集群计算 · 计算机科学 2023-11-23 Xianzhong Ding , Le Chen , Murali Emani , Chunhua Liao , Pei-Hung Lin , Tristan Vanderbruggen , Zhen Xie , Alberto E. Cerpa , Wan Du