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
Entailment-Driven Privacy Policy Classification with LLMs
Bhanuka Silva, Dishanika Denipitiyage, Suranga Seneviratne, Anirban Mahanti, Aruna Seneviratne
Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publications
Ethan Lin, Zhiyuan Peng, Yi Fang
Enhancing disease detection in radiology reports through fine-tuning lightweight LLM on weak labels
Yishu Wei, Xindi Wang, Hanley Ong, Yiliang Zhou, Adam Flanders, George Shih, Yifan Peng
Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference
Zongyue Qin, Zifan He, Neha Prakriya, Jason Cong, Yizhou Sun
Demystifying Issues, Causes and Solutions in LLM Open-Source Projects
Yangxiao Cai, Peng Liang, Yifei Wang, Zengyang Li, Mojtaba Shahin
Strategies for Improving NL-to-FOL Translation with LLMs: Data Generation, Incremental Fine-Tuning, and Verification
Ramya Keerthy Thatikonda, Jiuzhou Han, Wray Buntine, Ehsan Shareghi
A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions
Rajesh Ranjan, Shailja Gupta, Surya Narayan Singh
Design and Evaluation of a CDSS for Drug Allergy Management Using LLMs and Pharmaceutical Data Integration
Gabriele De Vito, Filomena Ferrucci, Athanasios Angelakis
Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs
Shadi Iskander, Nachshon Cohen, Zohar Karnin, Ori Shapira, Sofia Tolmach
Merging LoRAs like Playing LEGO: Pushing the Modularity of LoRA to Extremes Through Rank-Wise Clustering
Ziyu Zhao, Tao Shen, Didi Zhu, Zexi Li, Jing Su, Xuwu Wang, Kun Kuang, Fei Wu
Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework
Lu Chen, Ruqing Zhang, Jiafeng Guo, Yixing Fan, Xueqi Cheng
LLM With Tools: A Survey
Zhuocheng Shen
Bridging Speech and Text: Enhancing ASR with Pinyin-to-Character Pre-training in LLMs
Yang Yuhang, Peng Yizhou, Eng Siong Chng, Xionghu Zhong
Finetuning LLMs for Comparative Assessment Tasks
Vatsal Raina, Adian Liusie, Mark Gales
Predicting Distance matrix with large language models
Jiaxing Yang
HLB: Benchmarking LLMs' Humanlikeness in Language Use
Xufeng Duan, Bei Xiao, Xuemei Tang, Zhenguang G. Cai
Exploring the traditional NMT model and Large Language Model for chat translation
Jinlong Yang, Hengchao Shang, Daimeng Wei, Jiaxin Guo, Zongyao Li, Zhanglin Wu, Zhiqiang Rao, Shaojun Li, Yuhao Xie, Yuanchang Luo, Jiawei Zheng, Bin Wei, Hao Yang
Empirical Insights on Fine-Tuning Large Language Models for Question-Answering
Junjie Ye, Yuming Yang, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan
SwiftDossier: Tailored Automatic Dossier for Drug Discovery with LLMs and Agents
Gabriele Fossi, Youssef Boulaimena, Leila Outemzabeta, Nathalie Jeanraya, Stephane Gerarta, Sebastien Vachenca, Joanna Giemzaa, Salvatore Raieli
NER-Luxury: Named entity recognition for the fashion and luxury domain
Akim Mousterou