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
Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents
Yu Gu, Boyuan Zheng, Boyu Gou, Kai Zhang, Cheng Chang, Sanjari Srivastava, Yanan Xie, Peng Qi, Huan Sun, Yu Su
Probabilistic Consensus through Ensemble Validation: A Framework for LLM Reliability
Ninad Naik
Optimized Inference for 1.58-bit LLMs: A Time and Memory-Efficient Algorithm for Binary and Ternary Matrix Multiplication
Mohsen Dehghankar, Mahdi Erfanian, Abolfazl Asudeh
Multi-Document Financial Question Answering using LLMs
Shalin Shah, Srikanth Ryali, Ramasubbu Venkatesh
LLMs as Method Actors: A Model for Prompt Engineering and Architecture
Colin Doyle
VISTA: Visual Integrated System for Tailored Automation in Math Problem Generation Using LLM
Jeongwoo Lee, Kwangsuk Park, Jihyeon Park
Green My LLM: Studying the key factors affecting the energy consumption of code assistants
Tristan Coignion, Clément Quinton, Romain Rouvoy
Kwai-STaR: Transform LLMs into State-Transition Reasoners
Xingyu Lu, Yuhang Hu, Changyi Liu, Tianke Zhang, Zhenyu Yang, Zhixiang Ding, Shengsheng Qian, Meng Du, Ruiwen Kang, Kaiyu Tang, Fan Yang, Tingting Gao, Di Zhang, Hai-Tao Zheng, Bin Wen
Hands-On Tutorial: Labeling with LLM and Human-in-the-Loop
Ekaterina Artemova, Akim Tsvigun, Dominik Schlechtweg, Natalia Fedorova, Sergei Tilga, Boris Obmoroshev
LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAG
Laifa Tao, Qixuan Huang, Xianjun Wu, Weiwei Zhang, Yunlong Wu, Bin Li, Chen Lu, Xingshuo Hai
Enhancing classroom teaching with LLMs and RAG
Elizabeth A Mullins, Adrian Portillo, Kristalys Ruiz-Rohena, Aritran Piplai
YouTube Comments Decoded: Leveraging LLMs for Low Resource Language Classification
Aniket Deroy, Subhankar Maity
Evaluation data contamination in LLMs: how do we measure it and (when) does it matter?
Aaditya K. Singh, Muhammed Yusuf Kocyigit, Andrew Poulton, David Esiobu, Maria Lomeli, Gergely Szilvasy, Dieuwke Hupkes
Evaluating Moral Beliefs across LLMs through a Pluralistic Framework
Xuelin Liu, Yanfei Zhu, Shucheng Zhu, Pengyuan Liu, Ying Liu, Dong Yu
Extracting Unlearned Information from LLMs with Activation Steering
Atakan Seyitoğlu, Aleksei Kuvshinov, Leo Schwinn, Stephan Günnemann
On Targeted Manipulation and Deception when Optimizing LLMs for User Feedback
Marcus Williams, Micah Carroll, Adhyyan Narang, Constantin Weisser, Brendan Murphy, Anca Dragan