Large Language Model
Large language models (LLMs) are sophisticated AI systems designed to process and generate human-like text, aiming to improve various natural language processing tasks. Current research focuses on enhancing LLM safety, efficiency (through techniques like quantization and optimized decoding), and fairness, as well as improving their ability to perform complex reasoning and handle diverse instructions. These advancements are significant because they address critical limitations in current LLMs and pave the way for broader applications across diverse fields, including healthcare, legal tech, and autonomous systems.
Papers
HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations
Ziyu Wang, Hao Li, Di Huang, Amir M. Rahmani
See Where You Read with Eye Gaze Tracking and Large Language Model
Sikai Yang, Gang Yan, Wan Du
Zero-Shot Multi-Hop Question Answering via Monte-Carlo Tree Search with Large Language Models
Seongmin Lee, Jaewook Shin, Youngjin Ahn, Seokin Seo, Ohjoon Kwon, Kee-Eung Kim
Designing Domain-Specific Large Language Models: The Critical Role of Fine-Tuning in Public Opinion Simulation
Haocheng Lin
Performance Evaluation of Tokenizers in Large Language Models for the Assamese Language
Sagar Tamang, Dibya Jyoti Bora
HM3: Heterogeneous Multi-Class Model Merging
Stefan Hackmann
Uncovering Differences in Persuasive Language in Russian versus English Wikipedia
Bryan Li, Aleksey Panasyuk, Chris Callison-Burch
Confidential Prompting: Protecting User Prompts from Cloud LLM Providers
In Gim, Caihua Li, Lin Zhong
Implementing LLMs in industrial process modeling: Addressing Categorical Variables
Eleni D. Koronaki, Geremy Loachamin Suntaxi, Paris Papavasileiou, Dimitrios G. Giovanis, Martin Kathrein, Andreas G. Boudouvis, Stéphane P. A. Bordas
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models
Jiaming Li, Lei Zhang, Yunshui Li, Ziqiang Liu, yuelin bai, Run Luo, Longze Chen, Min Yang
AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow
Huizi Yu, Jiayan Zhou, Lingyao Li, Shan Chen, Jack Gallifant, Anye Shi, Xiang Li, Wenyue Hua, Mingyu Jin, Guang Chen, Yang Zhou, Zhao Li, Trisha Gupte, Ming-Li Chen, Zahra Azizi, Yongfeng Zhang, Themistocles L. Assimes, Xin Ma, Danielle S. Bitterman, Lin Lu, Lizhou Fan
Soft Measures for Extracting Causal Collective Intelligence
Maryam Berijanian, Spencer Dork, Kuldeep Singh, Michael Riley Millikan, Ashlin Riggs, Aadarsh Swaminathan, Sarah L. Gibbs, Scott E. Friedman, Nathan Brugnone
Mitigating Selection Bias with Node Pruning and Auxiliary Options
Hyeong Kyu Choi, Weijie Xu, Chi Xue, Stephanie Eckman, Chandan K. Reddy
A Survey on the Honesty of Large Language Models
Siheng Li, Cheng Yang, Taiqiang Wu, Chufan Shi, Yuji Zhang, Xinyu Zhu, Zesen Cheng, Deng Cai, Mo Yu, Lemao Liu, Jie Zhou, Yujiu Yang, Ngai Wong, Xixin Wu, Wai Lam
Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs
Gleb Mezentsev, Danil Gusak, Ivan Oseledets, Evgeny Frolov
"Why" Has the Least Side Effect on Model Editing
Tsung-Hsuan Pan, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
"Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Models
Ricardo Knauer, Mario Koddenbrock, Raphael Wallsberger, Nicholas M. Brisson, Georg N. Duda, Deborah Falla, David W. Evans, Erik Rodner
Research on Predicting Public Opinion Event Heat Levels Based on Large Language Models
Yi Ren, Tianyi Zhang, Weibin Li, DuoMu Zhou, Chenhao Qin, FangCheng Dong
A Survey on Complex Tasks for Goal-Directed Interactive Agents
Mareike Hartmann, Alexander Koller
SciDFM: A Large Language Model with Mixture-of-Experts for Science
Liangtai Sun, Danyu Luo, Da Ma, Zihan Zhao, Baocai Chen, Zhennan Shen, Su Zhu, Lu Chen, Xin Chen, Kai Yu