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

December 10, 2024

Filling Memory Gaps: Enhancing Continual Semantic Parsing via SQL Syntax Variance-Guided LLMs without Real Data Replay
Ruiheng Liu, Jinyu Zhang, Yanqi Song, Yu Zhang, Bailong Yang
Large Language Model Medical LLM Continual Learning Semantic Parsing Filling Gap Teacher Student Distillation Data Replay
IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language Model
Weizhen Bian, Siyan Liu, Yubo Zhou, Dezhi Chen, Yijie Liao, Zhenzhen Fan, Aobo Wang
Large Language Model Probabilistic Model Literature Search
EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models
Jialiang Cheng, Ning Gao, Yun Yue, Zhiling Ye, Jiadi Jiang, Jian Sha
Large Language Model Language Model LLM Training Training Method Local SGD Asynchronous Training Distributed Training
MAPLE: A Framework for Active Preference Learning Guided by Large Language Models
Saaduddin Mahmud, Mason Nakamura, Shlomo Zilberstein
Large Language Model Language Model New Framework Preference Learning Active Preference Preference Inference
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models
Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong, Wenping Hu, Guiguang Ding, Hui Chen, Jianwei Niu, Zijia Lin, Fuzheng Zhang, Di Zhang
Large Language Model Language Model Long Context Continual Pre Training Long Span Knowledge Barrier Long Context Task Single Stage Context Extrapolation
Predictable Emergent Abilities of LLMs: Proxy Tasks Are All You Need
Bo-Wen Zhang, Yan Yan, Boxiang Yang, Yifei Xue, Guang Liu
Large Language Model Medical LLM Model Ensemble Emergent Ability Relevance Ranking Weapon of Mass Destruction Proxy Stage Model
On Evaluating the Durability of Safeguards for Open-Weight LLMs
Xiangyu Qi, Boyi Wei, Nicholas Carlini, Yangsibo Huang, Tinghao Xie, Luxi He, Matthew Jagielski, Milad Nasr, Prateek Mittal, Peter Henderson
Large Language Model Threat Model Adversary Agent Adversarial Change

December 9, 2024

Large Language Models: An Applied Econometric Framework
Jens Ludwig, Sendhil Mullainathan, Ashesh Rambachan
Large Language Model Empirical Study Hypothesis Generation LLM Output Econometric Model
Assessing the Impact of Conspiracy Theories Using Large Language Models
Bohan Jiang, Dawei Li, Zhen Tan, Xinyi Zhou, Ashwin Rao, Kristina Lerman, H. Russell Bernard, Huan Liu
Large Language Model Global Impact Impact Assessment Human Right Conspiracy Theory
Leveraging Audio and Text Modalities in Mental Health: A Study of LLMs Performance
Abdelrahman A. Ali, Aya E. Fouda, Radwa J. Hanafy, Mohammed E. Fouda
Large Language Model Text Modality System Performance Mental Health Audio Driven Audio Modality
SUPERMERGE: An Approach For Gradient-Based Model Merging
Haoyu Yang, Zheng Zhang, Saket Sathe
Large Language Model Constructive Approach Task Specific Model Model Merging Merged Model
Mining Math Conjectures from LLMs: A Pruning Approach
Jake Chuharski, Elias Rojas Collins, Mark Meringolo
Large Language Model Counter Example Conjecture Refuting Algorithm Long Standing Conjecture
Training Large Language Models to Reason in a Continuous Latent Space
Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, Yuandong Tian
Large Language Model Chain of Thought Reason Giving Continuous Speech Latent Concept Large Language Model Training Continuous Latent Space
ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance
Chunwei Wang, Guansong Lu, Junwei Yang, Runhui Huang, Jianhua Han, Lu Hou, Wei Zhang, Hang Xu
Large Language Model Medical LLM Multimodal Large Language Model Multimodal Understanding Multimodal Alignment Artistic Drawing Low Self Esteem
Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy
Min Zeng, Caiquan Liu, Shiqi Zhang, Li Xie, Chen Sang, Xiaoxin Chen, Xiaoxin Chen
Large Language Model Full Model Text Classification Diversity Awareness Large Language Data Quality Text Classification Problem
Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families
Felipe Maia Polo, Seamus Somerstep, Leshem Choshen, Yuekai Sun, Mikhail Yurochkin
Large Language Model System Performance Scaling Law Multiplicative Size Scaling Single Parent Family LLM Capability Latent Skill
Small Languages, Big Models: A Study of Continual Training on Languages of Norway
David Samuel, Vladislav Mikhailov, Erik Velldal, Lilja Øvrelid, Lucas Georges Gabriel Charpentier, Andrey Kutuzov
Large Language Model Low Resource Language Large Model Generative Language Model Unknown Language Continual Training
The Rosetta Paradox: Domain-Specific Performance Inversions in Large Language Models
Basab Jha, Ujjwal Puri
Large Language Model Language Model Natural Language Processing Domain Specific Inversion Performance Fermi Paradox Domain Specificity
LLM-BIP: Structured Pruning for Large Language Models with Block-Wise Forward Importance Propagation
Haihang Wu
Large Language Model Edge Pruning Structural Pruning Importance Weighting LLM RBMT Layer Wise Pruning
Political-LLM: Large Language Models in Political Science
Lincan Li, Jiaqi Li, Catherine Chen, Fred Gui, Hongjia Yang, Chenxiao Yu, Zhengguang Wang, Jianing Cai, Junlong Aaron Zhou, Bolin Shen, Alex Qian, Weixin Chen, Zhongkai Xue, Lichao Sun, Lifang He, Hanjie Chen, Kaize Ding, Zijian Du, Fangzhou Mu, Jiaxin Pei, Jieyu Zhao, Swabha Swayamdipta, Willie Neiswanger, Hua Wei, Xiyang Hu, Shixiang Zhu, Tianlong Chen, Yingzhou Lu, Yang Shi, Lianhui Qin, Tianfan Fu, Zhengzhong Tu, Yuzhe Yang, Jaemin Yoo, Jiaheng Zhang, Ryan Rossi, Liang Zhan, Liang Zhao, Emilio Ferrara, Yan Liu, Furong Huang, Xiangliang Zhang, Lawrence Rothenberg, Shuiwang Ji, Philip S. Yu, Yue Zhao, Yushun Dong
Large Language Model Medical LLM Social Science

Large Language Model

Papers

Filling Memory Gaps: Enhancing Continual Semantic Parsing via SQL Syntax Variance-Guided LLMs without Real Data Replay

IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language Model

EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models

MAPLE: A Framework for Active Preference Learning Guided by Large Language Models

Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models

Predictable Emergent Abilities of LLMs: Proxy Tasks Are All You Need

On Evaluating the Durability of Safeguards for Open-Weight LLMs

Large Language Models: An Applied Econometric Framework

Assessing the Impact of Conspiracy Theories Using Large Language Models

Leveraging Audio and Text Modalities in Mental Health: A Study of LLMs Performance

SUPERMERGE: An Approach For Gradient-Based Model Merging

Mining Math Conjectures from LLMs: A Pruning Approach

Training Large Language Models to Reason in a Continuous Latent Space

ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy

Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families

Small Languages, Big Models: A Study of Continual Training on Languages of Norway

The Rosetta Paradox: Domain-Specific Performance Inversions in Large Language Models

LLM-BIP: Structured Pruning for Large Language Models with Block-Wise Forward Importance Propagation

Political-LLM: Large Language Models in Political Science