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
E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models
Chan Kim, Keonwoo Kim, Mintaek Oh, Hanbi Baek, Jiyang Lee, Donghwi Jung, Soojin Woo, Younkyung Woo, John Tucker, Roya Firoozi, Seung-Woo Seo, Mac Schwager, Seong-Woo Kim
HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Making
Sumera Anjum, Hanzhi Zhang, Wenjun Zhou, Eun Jin Paek, Xiaopeng Zhao, Yunhe Feng
Comprehensive Study on Sentiment Analysis: From Rule-based to modern LLM based system
Shailja Gupta, Rajesh Ranjan, Surya Narayan Singh
SFR-RAG: Towards Contextually Faithful LLMs
Xuan-Phi Nguyen, Shrey Pandit, Senthil Purushwalkam, Austin Xu, Hailin Chen, Yifei Ming, Zixuan Ke, Silvio Savarese, Caiming Xong, Shafiq Joty
Rediscovering the Latent Dimensions of Personality with Large Language Models as Trait Descriptors
Joseph Suh, Suhong Moon, Minwoo Kang, David M. Chan
GP-GPT: Large Language Model for Gene-Phenotype Mapping
Yanjun Lyu, Zihao Wu, Lu Zhang, Jing Zhang, Yiwei Li, Wei Ruan, Zhengliang Liu, Xiaowei Yu, Chao Cao, Tong Chen, Minheng Chen, Yan Zhuang, Xiang Li, Rongjie Liu, Chao Huang, Wentao Li, Tianming Liu, Dajiang Zhu
Causal Inference with Large Language Model: A Survey
Jing Ma
Efficient Hybrid Inference for LLMs: Reward-Based Token Modelling with Selective Cloud Assistance
Adarsh MS, Jithin VG, Ditto PS
AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs
Madhusudan Ghosh, Shrimon Mukherjee, Asmit Ganguly, Partha Basuchowdhuri, Sudip Kumar Naskar, Debasis Ganguly
Confidence Estimation for LLM-Based Dialogue State Tracking
Yi-Jyun Sun, Suvodip Dey, Dilek Hakkani-Tur, Gokhan Tur
Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting
Jianfei Wu, Xubin Wang, Weijia Jia
Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora
Yungi Kim, Hyunsoo Ha, Sukyung Lee, Jihoo Kim, Seonghoon Yang, Chanjun Park
Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model
Bo-Kai Ruan, Hao-Tang Tsui, Yung-Hui Li, Hong-Han Shuai
Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
Alireza Salemi, Hamed Zamani
LLM-Powered Ensemble Learning for Paper Source Tracing: A GPU-Free Approach
Kunlong Chen, Junjun Wang, Zhaoqun Chen, Kunjin Chen, Yitian Chen
Symbolic Regression with a Learned Concept Library
Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, Miles Cranmer, Swarat Chaudhuri
PeriGuru: A Peripheral Robotic Mobile App Operation Assistant based on GUI Image Understanding and Prompting with LLM
Kelin Fu, Yang Tian, Kaigui Bian
Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation
Hui Yi Leong, Yi Fan Gao, Ji Shuai, Uktu Pamuksuz
Contextual Evaluation of Large Language Models for Classifying Tropical and Infectious Diseases
Mercy Asiedu, Nenad Tomasev, Chintan Ghate, Tiya Tiyasirichokchai, Awa Dieng, Oluwatosin Akande, Geoffrey Siwo, Steve Adudans, Sylvanus Aitkins, Odianosen Ehiakhamen, Katherine Heller
Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversation
Cheng Charles Ma, Kevin Hyekang Joo, Alexandria K. Vail, Sunreeta Bhattacharya, Álvaro Fernández García, Kailana Baker-Matsuoka, Sheryl Mathew, Lori L. Holt, Fernando De la Torre