Medical LLM
Medical LLMs are large language models adapted for healthcare applications, primarily aiming to improve medical information access, analysis, and decision-making. Current research focuses on enhancing reasoning capabilities through techniques like chain-of-thought prompting and dynamic reasoning trajectory search, as well as addressing biases and ensuring safety through careful preference alignment and guardrail implementation. These advancements hold significant promise for improving healthcare efficiency and patient care, but ongoing work is crucial to address challenges like bias mitigation, hallucination reduction, and robust evaluation in real-world clinical settings.
Papers
PIANIST: Learning Partially Observable World Models with LLMs for Multi-Agent Decision Making
Jonathan Light, Sixue Xing, Yuanzhe Liu, Weiqin Chen, Min Cai, Xiusi Chen, Guanzhi Wang, Wei Cheng, Yisong Yue, Ziniu Hu
LLMs Do Not Think Step-by-step In Implicit Reasoning
Yijiong Yu
DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration
Sizhe Liu, Yizhou Lu, Siyu Chen, Xiyang Hu, Jieyu Zhao, Tianfan Fu, Yue Zhao
Enabling Efficient Serverless Inference Serving for LLM (Large Language Model) in the Cloud
Himel Ghosh
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet, Peerat Limkonchotiwat
Automatic High-quality Verilog Assertion Generation through Subtask-Focused Fine-Tuned LLMs and Iterative Prompting
Mohammad Shahidzadeh, Behnam Ghavami, Steve Wilton, Lesley Shannon
The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judges
Jiqun Liu, Jiangen He
Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation
Colin Diggs, Michael Doyle, Amit Madan, Siggy Scott, Emily Escamilla, Jacob Zimmer, Naveed Nekoo, Paul Ursino, Michael Bartholf, Zachary Robin, Anand Patel, Chris Glasz, William Macke, Paul Kirk, Jasper Phillips, Arun Sridharan, Doug Wendt, Scott Rosen, Nitin Naik, Justin F. Brunelle, Samruddhi Thaker
LLM for Barcodes: Generating Diverse Synthetic Data for Identity Documents
Hitesh Laxmichand Patel, Amit Agarwal, Bhargava Kumar, Karan Gupta, Priyaranjan Pattnayak
Universal and Context-Independent Triggers for Precise Control of LLM Outputs
Jiashuo Liang, Guancheng Li, Yang Yu
Multiverse of Greatness: Generating Story Branches with LLMs
Pittawat Taveekitworachai, Chollakorn Nimpattanavong, Mustafa Can Gursesli, Antonio Lanata, Andrea Guazzini, Ruck Thawonmas
Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework
Mengshuo Jia, Zeyu Cui, Gabriela Hug
LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issues
Yalan Lin, Yingwei Ma, Rongyu Cao, Binhua Li, Fei Huang, Xiaodong Gu, Yongbin Li
A Framework for Evaluating LLMs Under Task Indeterminacy
Luke Guerdan, Hanna Wallach, Solon Barocas, Alexandra Chouldechova
LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training
Shreen Gul, Mohamed Elmahallawy, Sanjay Madria, Ardhendu Tripathy
Unification of Balti and trans-border sister dialects in the essence of LLMs and AI Technology
Muhammad Sharif, Jiangyan Yi, Muhammad Shoaib
Mediating Modes of Thought: LLM's for design scripting
Moritz Rietschel, Fang Guo, Kyle Steinfeld
Enhancing Multi-Class Disease Classification: Neoplasms, Cardiovascular, Nervous System, and Digestive Disorders Using Advanced LLMs
Ahmed Akib Jawad Karim, Muhammad Zawad Mahmud, Samiha Islam, Aznur Azam
Enhanced Sign Language Translation between American Sign Language (ASL) and Indian Sign Language (ISL) Using LLMs
Malay Kumar, S. Sarvajit Visagan, Tanish Sarang Mahajan, Anisha Natarajan