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

October 8, 2024

Are Large Language Models State-of-the-art Quality Estimators for Machine Translation of User-generated Content?
Shenbin Qian, Constantin Orăsan, Diptesh Kanojia, Félix do Carmo
Large Language Model Language Model Machine Translation User Generated Content Quality Estimation Annotation Error
Auto-Evolve: Enhancing Large Language Model's Performance via Self-Reasoning Framework
Krishna Aswani, Huilin Lu, Pranav Patankar, Priya Dhalwani, Iris Tan, Jayant Ganeshmohan, Simon Lacasse
Large Language Model System Performance Self Reasoning Reasoning Module Reasoning Paradigm
In-Context Code-Text Learning for Bimodal Software Engineering
Xunzhu Tang, Liran Wang, Yonghui Liu, Linzheng Chai, Jian Yang, Zhoujun Li, Haoye Tian, Jacques Klein, Tegawende F. Bissyande
Large Language Model Prompt Learning Code Representation Code Metric
MC-MoE: Mixture Compressor for Mixture-of-Experts LLMs Gains More
Wei Huang, Yue Liao, Jianhui Liu, Ruifei He, Haoru Tan, Shiming Zhang, Hongsheng Li, Si Liu, Xiaojuan Qi
Large Language Model Language Model Mixture of Expert Mixed Precision Quantization Dynamic Pruning Compressor Based Machine
EVOLvE: Evaluating and Optimizing LLMs For Exploration
Allen Nie, Yi Su, Bo Chang, Jonathan N. Lee, Ed H. Chi, Quoc V. Le, Minmin Chen
Large Language Model Environment Exploration Contextual Bandit Model Free Reinforcement Learning Based Exploration Meta EVOLVE
Entering Real Social World! Benchmarking the Theory of Mind and Socialization Capabilities of LLMs from a First-person Perspective
Guiyang Hou, Wenqi Zhang, Yongliang Shen, Zeqi Tan, Sihao Shen, Weiming Lu
Large Language Model Human Mind Social Interaction Egocentric View First Person Social Simulation
Temporal Reasoning Transfer from Text to Video
Lei Li, Yuanxin Liu, Linli Yao, Peiyuan Zhang, Chenxin An, Lean Wang, Xu Sun, Lingpeng Kong, Qi Liu
Large Language Model Text Modality Video Understanding Source Video Temporal Reasoning Temporal Understanding
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning
Hao Ma, Tianyi Hu, Zhiqiang Pu, Boyin Liu, Xiaolin Ai, Yanyan Liang, Min Chen
Large Language Model Reinforcement Learning Fine Tuning Multi Agent Reinforcement Learning LLM Fine Tuning Adaptive Coevolution Sequential Decision Making Policy
Decoding Decoded: Understanding Hyperparameter Effects in Open-Ended Text Generation
Esteban Garces Arias, Meimingwei Li, Christian Heumann, Matthias Aßenmacher
Large Language Model Text Generation Hyperparameter Tuning Accurate Decoding Text Generation Task Hyperparameter Selection Open Ended Text Generation Hyperparameter Importance
TOWER: Tree Organized Weighting for Evaluating Complex Instructions
Noah Ziems, Zhihan Zhang, Meng Jiang
Large Language Model Complex Instruction
Training-free LLM-generated Text Detection by Mining Token Probability Sequences
Yihuai Xu, Yongwei Wang, Yifei Bi, Huangsen Cao, Zhouhan Lin, Yu Zhao, Fei Wu
Large Language Model Text Detection LLM Generated Text Multilingual Detection
PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches
Rana Muhammad Shahroz Khan, Pingzhi Li, Sukwon Yun, Zhenyu Wang, Shahriar Nirjon, Chau-Wai Wong, Tianlong Chen
Large Language Model Fine Tuning Domain Knowledge Parameter Efficient Fine Tuning Training Free Domain Specific Task
Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging
Ryota Tozuka, Hisashi Johno, Akitomo Amakawa, Junichi Sato, Mizuki Muto, Shoichiro Seki, Atsushi Komaba, Hiroshi Onishi
Large Language Model Application Proficiency Retrieval Augmented Generation
Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG
Bowen Jin, Jinsung Yoon, Jiawei Han, Sercan O. Arik
Large Language Model Technical Challenge Retrieval Augmented Generation LLM Fine Tuning Input Sequence Long Context Large Language Model Long Input
Active Evaluation Acquisition for Efficient LLM Benchmarking
Yang Li, Jie Ma, Miguel Ballesteros, Yassine Benajiba, Graham Horwood
Large Language Model LLM Benchmark LLM Performance Large Scale Benchmark Efficient Evaluation Active Acquisition
RRADistill: Distilling LLMs' Passage Ranking Ability for Document Re-Ranking of Long-Tail Queries in a Search Engine
Nayoung Choi, Youngjune Lee, Gyu-Hwung Cho, Haeyu Jeong, Jungmin Kong, Saehun Kim, Keunchan Park, Jaeho Choi, Sarah Cho, Inchang Jeong, Gyohee Nam, Sunghoon Han, Wonil Yang
Large Language Model Search Engine Long Tail Passage Reranking Label Generation Ranking Step
Time Transfer: On Optimal Learning Rate and Batch Size In The Infinite Data Limit
Oleg Filatov, Jan Ebert, Jiangtao Wang, Stefan Kesselheim
Large Language Model Learning Rate Large Model Data Stream Batch Size Model Scaling Adaptive Batch Size Optimal Scaling
Gradual Learning: Optimizing Fine-Tuning with Partially Mastered Knowledge in Large Language Models
Bozhou Li, Hao Liang, Yang Li, Fangcheng Fu, Hongzhi Yin, Conghui He, Wentao Zhang
Large Language Model Fine Tuning Knowledge Based Gradual Domain Adaptation Knowledge Retention
CodeCipher: Learning to Obfuscate Source Code Against LLMs
Yalan Lin, Chengcheng Wan, Yixiong Fang, Xiaodong Gu
Large Language Model Medical LLM Real World Code Source Code Obfuscation Technique Privacy Issue
LightRAG: Simple and Fast Retrieval-Augmented Generation
Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, Chao Huang
Large Language Model Retrieval Augmented Generation Context Information Information Retrieval

Large Language Model

Papers

Are Large Language Models State-of-the-art Quality Estimators for Machine Translation of User-generated Content?

Auto-Evolve: Enhancing Large Language Model's Performance via Self-Reasoning Framework

In-Context Code-Text Learning for Bimodal Software Engineering

MC-MoE: Mixture Compressor for Mixture-of-Experts LLMs Gains More

EVOLvE: Evaluating and Optimizing LLMs For Exploration

Entering Real Social World! Benchmarking the Theory of Mind and Socialization Capabilities of LLMs from a First-person Perspective

Temporal Reasoning Transfer from Text to Video

Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning

Decoding Decoded: Understanding Hyperparameter Effects in Open-Ended Text Generation

TOWER: Tree Organized Weighting for Evaluating Complex Instructions

Training-free LLM-generated Text Detection by Mining Token Probability Sequences

PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches

Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging

Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG

Active Evaluation Acquisition for Efficient LLM Benchmarking

RRADistill: Distilling LLMs' Passage Ranking Ability for Document Re-Ranking of Long-Tail Queries in a Search Engine

Time Transfer: On Optimal Learning Rate and Batch Size In The Infinite Data Limit

Gradual Learning: Optimizing Fine-Tuning with Partially Mastered Knowledge in Large Language Models

CodeCipher: Learning to Obfuscate Source Code Against LLMs

LightRAG: Simple and Fast Retrieval-Augmented Generation