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 9, 2024

CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models
Zi Gong, Hang Yu, Cong Liao, Bingchang Liu, Chaoyu Chen, Jianguo Li
Large Language Model Multi Task Learning Multi Task Convergence Behavior Convergence Performance Multitask Finetuning
Steering Large Language Models using Conceptors: Improving Addition-Based Activation Engineering
Joris Postmus, Steven Abreu
Large Language Model Language Model High Impact Concept Activation Pattern Steering Vector Activation Vector Advanced Activation Mechanism
Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of LLMs with Situation Puzzles
Qi Chen, Bowen Zhang, Gang Wang, Qi Wu
Large Language Model Puzzle Solving Lateral Thinking
Improving Data Efficiency via Curating LLM-Driven Rating Systems
Jinlong Pang, Jiaheng Wei, Ankit Parag Shah, Zhaowei Zhu, Yaxuan Wang, Chen Qian, Yang Liu, Yujia Bao, Wei Wei
Large Language Model Data Quality LLM Evaluation Data Efficiency
Guaranteed Generation from Large Language Models
Minbeom Kim, Thibaut Thonet, Jos Rozen, Hwaran Lee, Kyomin Jung, Marc Dymetman
Large Language Model Text Generation Text Generation Capability Lexical Constraint Unconditional Generation
Learning Evolving Tools for Large Language Models
Guoxin Chen, Zhong Zhang, Xin Cong, Fangda Guo, Yesai Wu, Yankai Lin, Wenzheng Feng, Yasheng Wang
Large Language Model Tool Learning
Dissecting Fine-Tuning Unlearning in Large Language Models
Yihuai Hong, Yuelin Zou, Lijie Hu, Ziqian Zeng, Di Wang, Haiqin Yang
Large Language Model Unlearning Framework Unlearning Method Parameter Space Reconstruction
TuringQ: Benchmarking AI Comprehension in Theory of Computation
Pardis Sadat Zahraei, Ehsaneddin Asgari
Large Language Model Theoretical Understanding Reasoning Capability Computation Method Complex Reasoning Task Question Answer Pair Turing Machine
Chip-Tuning: Classify Before Language Models Say
Fangwei Zhu, Dian Li, Jiajun Huang, Gang Liu, Hui Wang, Zhifang Sui
Large Language Model Language Model Fine Tuning Structured Pruning Hyper Tune
TorchTitan: One-stop PyTorch native solution for production ready LLM pre-training
Wanchao Liang, Tianyu Liu, Less Wright, Will Constable, Andrew Gu, Chien-Chin Huang, Iris Zhang, Wei Feng, Howard Huang, Junjie Wang, Sanket Purandare, Gokul Nadathur, Stratos Idreos
Large Language Model Potential Scalability Pytorch Model Single GPU Production Incident LLM Training Multi GPU Training Optimization
AuditWen:An Open-Source Large Language Model for Audit
Jiajia Huang, Haoran Zhu, Chao Xu, Tianming Zhan, Qianqian Xie, Jimin Huang
Large Language Model Language Model Open Source Audit Evidence Cybersecurity Domain Artificial Intelligence Audit Qwen Audio
Composite Learning Units: Generalized Learning Beyond Parameter Updates to Transform LLMs into Adaptive Reasoners
Santosh Kumar Radha, Oktay Goktas
Large Language Model Machine Learning Complex Reasoning Task Specific Reasoning System Parameter Update Dynamic Reasoning
Large Language Model Compression with Neural Architecture Search
Rhea Sanjay Sukthanker, Benedikt Staffler, Frank Hutter, Aaron Klein
Large Language Model Language Model Neural Architecture Search Inference Cost Structural Pruning LLM Compression
Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations
Tarun Raheja, Nilay Pochhi
Large Language Model Language Model Recent Advance Barzilai Borwein Technique Red Teaming Attack Strategy Attack Method Ethical Consideration
MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data
Mingu Kang, Dongseok Lee, Woojin Cho, Jaehyeon Park, Kookjin Lee, Anthony Gruber, Youngjoon Hong, Noseong Park
Large Language Model Neural Network Context Learning Foundational Model Noise Prior Advection Diffusion Reaction
DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Response
Rajat Rawat
Large Language Model New Benchmark System Performance Natural Disaster Disaster Response
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs
Ruijia Niu, Dongxia Wu, Rose Yu, Yi-An Ma
Large Language Model Epistemic Uncertainty

October 8, 2024

A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models
Cong Guo, Feng Cheng, Zhixu Du, James Kiessling, Jonathan Ku, Shiyu Li, Ziru Li, Mingyuan Ma, Tergel Molom-Ochir, Benjamin Morris, Haoxuan Shan, Jingwei Sun, Yitu Wang, Chiyue Wei, Xueying Wu, Yuhao Wu, Hao Frank Yang, Jingyang Zhang, Junyao Zhang, Qilin Zheng, Guanglei Zhou, Hai (Helen)Li, Yiran Chen
Large Language Model Natural Language Processing Timely Survey New Era Software Engineering Computational Resource Traditional Deep Learning Collaborative Machine
SpaLLM: Unified Compressive Adaptation of Large Language Models with Sketching
Tianyi Zhang, Junda Su, Oscar Wu, Zhaozhuo Xu, Anshumali Shrivastava
Large Language Model LLM Fine Tuning Creative Sketching Compressive Learning
ToolBridge: An Open-Source Dataset to Equip LLMs with External Tool Capabilities
Zhenchao Jin, Mengchen Liu, Dongdong Chen, Lingting Zhu, Yunsheng Li, Lequan Yu
Large Language Model Training Data Open Source Capability Evolution Flexible Toolkit Evaluation Datasets Pedagogical Agent

Large Language Model

Papers

CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models

Steering Large Language Models using Conceptors: Improving Addition-Based Activation Engineering

Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of LLMs with Situation Puzzles

Improving Data Efficiency via Curating LLM-Driven Rating Systems

Guaranteed Generation from Large Language Models

Learning Evolving Tools for Large Language Models

Dissecting Fine-Tuning Unlearning in Large Language Models

TuringQ: Benchmarking AI Comprehension in Theory of Computation

Chip-Tuning: Classify Before Language Models Say

TorchTitan: One-stop PyTorch native solution for production ready LLM pre-training

AuditWen:An Open-Source Large Language Model for Audit

Composite Learning Units: Generalized Learning Beyond Parameter Updates to Transform LLMs into Adaptive Reasoners

Large Language Model Compression with Neural Architecture Search

Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations

MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data

DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Response

Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs

A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models

SpaLLM: Unified Compressive Adaptation of Large Language Models with Sketching

ToolBridge: An Open-Source Dataset to Equip LLMs with External Tool Capabilities