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 19, 2024

Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems
Genki Kusano, Kosuke Akimoto, Kunihiro Takeoka
Large Language Model Full Model Complex Prompt Recommendation System Large Language Recommendation Task Natural Language Input Recommendation Accuracy
ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study
Eric Modesitt, Ke Yang, Spencer Hulsey, Chengxiang Zhai, Volodymyr Kindratenko
Large Language Model Domain Specific Domain Knowledge Adaptation Concern Data Curation Orbital Motion Domain Specific Large Language Model
All-in-One Tuning and Structural Pruning for Domain-Specific LLMs
Lei Lu, Zhepeng Wang, Ruexue Bao, Mengbing Wang, Fangyi Li, Yawen Wu, Weiwen Jiang, Jie Xu, Yanzhi Wang, Shangqian Gao
Large Language Model Structural Pruning Domain Specific Large Language Model

December 18, 2024

Clinical Trials Ontology Engineering with Large Language Models
Berkan Çakır
Large Language Model Top Level Ontology Clinical Trial
ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals
Utkarsh Saxena, Sayeh Sharify, Kaushik Roy, Xin Wang
Large Language Model Post Training Quantization Multiplier Free Quantization Mixed Precision Quantization Quantization Error
Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting
Vijay Goyal, Mustafa Khan, Aprameya Tirupati, Harveer Saini, Michael Lam, Kevin Zhu
Large Language Model Natural Language Processing Knowledge Distillation
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs
David Restrepo, Chenwei Wu, Zhengxu Tang, Zitao Shuai, Thao Nguyen Minh Phan, Jun-En Ding, Cong-Tinh Dao, Jack Gallifant, Robyn Gayle Dychiao, Jose Carlo Artiaga, André Hiroshi Bando, Carolina Pelegrini Barbosa Gracitelli, Vincenz Ferrer, Leo Anthony Celi, Danielle Bitterman, Michael G Morley, Luis Filipe Nakayama
Large Language Model Medical LLM Natural Language Chain of Thought Low Resource Direct Assessment Multilingual Benchmark Multilingual Bias
Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining
Steven Feng, Shrimai Prabhumoye, Kezhi Kong, Dan Su, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
Large Language Model Raw Data Balancing Efficiency Multi Phase Data Mixture Blending Method LLM Accuracy Data Mixing
LLMs can realize combinatorial creativity: generating creative ideas via LLMs for scientific research
Tianyang Gu, Jingjin Wang, Zhihao Zhang, HaoHong Li
Large Language Model Scientific Research Computational Creativity Idea Generation Creative Thought
Alignment faking in large language models
Ryan Greenblatt, Carson Denison, Benjamin Wright, Fabien Roger, Monte MacDiarmid, Sam Marks, Johannes Treutlein, Tim Belonax, Jack Chen, David Duvenaud, Akbir Khan, Julian Michael, Sören Mindermann, Ethan Perez, Linda Petrini, Jonathan Uesato, Jared Kaplan, Buck Shlegeris, Samuel R. Bowman, Evan Hubinger
Large Language Model Alignment Problem Synthetic to Real Fake Alignment
Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation
Vera Neplenbroek, Arianna Bisazza, Raquel Fernández
Large Language Model Language Generation Cross Lingual Transfer Generative Large Language Model Self Debiasing Language Bias Fine Grained Detoxification
Hansel: Output Length Controlling Framework for Large Language Models
Seoha Song, Junhyun Lee, Hyeonmok Ko
Large Language Model Length Control
Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes
Katarzyna Kobalczyk, Claudio Fanconi, Hao Sun, Mihaela van der Schaar
Large Language Model Reward Model Human Preference LLM Fine Tuning Reward Report Neural Process LLM Policy Heterogeneous Preference
Channel Merging: Preserving Specialization for Merged Experts
Mingyang Zhang, Jing Liu, Ganggui Ding, Xinyi Yu, Linlin Ou, Bohan Zhuang
Large Language Model Model Merging Task Specialization Merging Expert
A Systematic Examination of Preference Learning through the Lens of Instruction-Following
Joongwon Kim, Anirudh Goyal, Aston Zhang, Bo Xiong, Rui Hou, Melanie Kambadur, Dhruv Mahajan, Hannaneh Hajishirzi, Liang Tan
Large Language Model Camera Lens Preference Learning Preference Pair Systematic Study Preference Datasets
Pipeline Analysis for Developing Instruct LLMs in Low-Resource Languages: A Case Study on Basque
Ander Corral, Ixak Sarasua, Xabier Saralegi
Large Language Model Language Understanding Low Resource Language Low Resource Unknown Language NLU Model Data Analysis Pipeline
Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali
Sharad Duwal, Suraj Prasai, Suresh Manandhar
Large Language Model Continual LEArning Global Evaluation Low Resource Linguistic Knowledge Domain Incremental Continual Learning
Zero-Shot Prompting and Few-Shot Fine-Tuning: Revisiting Document Image Classification Using Large Language Models
Anna Scius-Bertrand, Michael Jungo, Lars Vögtlin, Jean-Marc Spat, Andreas Fischer
Large Language Model Classification Code Document Understanding Document Image Annotated Training Data Zero Shot Prompting Shot Fine Tuning
Meta-Reflection: A Feedback-Free Reflection Learning Framework
Yaoke Wang, Yun Zhu, Xintong Bao, Wenqiao Zhang, Suyang Dai, Kehan Chen, Wenqiang Li, Gang Huang, Siliang Tang, Yueting Zhuang
Large Language Model Language Understanding Reflection Flow
Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenization
Guanghan Li, Xun Zhang, Yufei Zhang, Yifan Yin, Guojun Yin, Wei Lin
Large Language Model Alignment Problem Multi Stage Traditional Recommender Latent Direction Federated Recommendation

Large Language Model

Papers

Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems

ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs

Clinical Trials Ontology Engineering with Large Language Models

ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals

Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting

Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs

Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining

LLMs can realize combinatorial creativity: generating creative ideas via LLMs for scientific research

Alignment faking in large language models

Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation

Hansel: Output Length Controlling Framework for Large Language Models

Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes

Channel Merging: Preserving Specialization for Merged Experts

A Systematic Examination of Preference Learning through the Lens of Instruction-Following

Pipeline Analysis for Developing Instruct LLMs in Low-Resource Languages: A Case Study on Basque

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali

Zero-Shot Prompting and Few-Shot Fine-Tuning: Revisiting Document Image Classification Using Large Language Models

Meta-Reflection: A Feedback-Free Reflection Learning Framework

Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenization