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
Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles
Yuxi Xia, Pedro Henrique Luz de Araujo, Klim Zaporojets, Benjamin Roth
Localizing AI: Evaluating Open-Weight Language Models for Languages of Baltic States
Jurgita Kapočiūtė-Dzikienė, Toms Bergmanis, Mārcis Pinnis
Not all tokens are created equal: Perplexity Attention Weighted Networks for AI generated text detection
Pablo Miralles-González, Javier Huertas-Tato, Alejandro Martín, David Camacho
Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study
Ramya Jonnala, Gongbo Liang, Jeong Yang, Izzat Alsmadi
More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives
Xiaoqing Zhang, Ang Lv, Yuhan Liu, Flood Sung, Wei Liu, Shuo Shang, Xiuying Chen, Rui Yan
ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono
Jingquan Wang, Harry Zhang, Khailanii Slaton, Shu Wang, Radu Serban, Jinlong Wu, Dan Negrut
SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment
Yuchun Fan, Yongyu Mu, Yilin Wang, Lei Huang, Junhao Ruan, Bei Li, Tong Xiao, Shujian Huang, Xiaocheng Feng, Jingbo Zhu
Applying Large Language Models in Knowledge Graph-based Enterprise Modeling: Challenges and Opportunities
Benedikt Reitemeyer, Hans-Georg Fill
PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models
Lingzhi Yuan, Xinfeng Li, Chejian Xu, Guanhong Tao, Xiaojun Jia, Yihao Huang, Wei Dong, Yang Liu, XiaoFeng Wang, Bo Li
A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models
Shuyang Wang, Somayeh Moazeni, Diego Klabjan
Align-Pro: A Principled Approach to Prompt Optimization for LLM Alignment
Prashant Trivedi, Souradip Chakraborty, Avinash Reddy, Vaneet Aggarwal, Amrit Singh Bedi, George K. Atia
Reading with Intent -- Neutralizing Intent
Benjamin Reichman, Adar Avsian, Larry Heck
CLIX: Cross-Lingual Explanations of Idiomatic Expressions
Aaron Gluck, Katharina von der Wense, Maria Pacheco
Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches
Alhassan Mumuni, Fuseini Mumuni
LiLMaps: Learnable Implicit Language Maps
Evgenii Kruzhkov, Sven Behnke
Registering Source Tokens to Target Language Spaces in Multilingual Neural Machine Translation
Zhi Qu, Yiran Wang, Jiannan Mao, Chenchen Ding, Hideki Tanaka, Masao Utiyama, Taro Watanabe
IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment
Yiming Zhang, Zheng Chang, Wentao Cai, MengXing Ren, Kang Yuan, Yining Sun, Zenghui Ding
Graph-based Retrieval Augmented Generation for Dynamic Few-shot Text Classification
Yubo Wang, Haoyang Li, Fei Teng, Lei Chen
CodeVision: Detecting LLM-Generated Code Using 2D Token Probability Maps and Vision Models
Zhenyu Xu, Victor S. Sheng
Visual Large Language Models for Generalized and Specialized Applications
Yifan Li, Zhixin Lai, Wentao Bao, Zhen Tan, Anh Dao, Kewei Sui, Jiayi Shen, Dong Liu, Huan Liu, Yu Kong