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
Push the Limit of Multi-modal Emotion Recognition by Prompting LLMs with Receptive-Field-Aware Attention Weighting
Liyun Zhang, Dian Ding, Yu Lu, Yi-Chao Chen, Guangtao Xue
Synthetic Data Generation with LLM for Improved Depression Prediction
Andrea Kang, Jun Yu Chen, Zoe Lee-Youngzie, Shuhao Fu
On Limitations of LLM as Annotator for Low Resource Languages
Suramya Jadhav, Abhay Shanbhag, Amogh Thakurdesai, Ridhima Sinare, Raviraj Joshi
Inference Scaling $\scriptsize\mathtt{F}$Laws: The Limits of LLM Resampling with Imperfect Verifiers
Benedikt Stroebl, Sayash Kapoor, Arvind Narayanan
Can LLMs be Good Graph Judger for Knowledge Graph Construction?
Haoyu Huang, Chong Chen, Conghui He, Yang Li, Jiawei Jiang, Wentao Zhang
Blockchain Meets LLMs: A Living Survey on Bidirectional Integration
Jianghao Gong, Peiqi Yan, Yue Zhang, Hongli An, Logan Liu
The Two-Hop Curse: LLMs trained on A->B, B->C fail to learn A-->C
Mikita Balesni, Tomek Korbak, Owain Evans
What can LLM tell us about cities?
Zhuoheng Li, Yaochen Wang, Zhixue Song, Yuqi Huang, Rui Bao, Guanjie Zheng, Zhenhui Jessie Li
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