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

September 24, 2024

Small Language Models: Survey, Measurements, and Insights
Zhenyan Lu, Xiang Li, Dongqi Cai, Rongjie Yi, Fangming Liu, Xiwen Zhang, Nicholas D. Lane, Mengwei Xu
Large Language Model Language Model Timely Survey DCU Insight AQ Open Source LLM Decoder Only Language Model Internal Measurement
CHBench: A Chinese Dataset for Evaluating Health in Large Language Models
Chenlu Guo, Nuo Xu, Yi Chang, Yuan Wu
Large Language Model Performance Evaluation Chinese Dataset Chinese Benchmark Health Information
XTRUST: On the Multilingual Trustworthiness of Large Language Models
Yahan Li, Yi Wang, Yi Chang, Yuan Wu
Large Language Model Language Model Natural Language Processing Multilingual Safety
Federated Large Language Models: Current Progress and Future Directions
Yuhang Yao, Jianyi Zhang, Junda Wu, Chengkai Huang, Yu Xia, Tong Yu, Ruiyi Zhang, Sungchul Kim, Ryan Rossi, Ang Li, Lina Yao, Julian McAuley, Yiran Chen, Carlee Joe-Wong
Large Language Model Training Data Federated Learning Future Direction Federated Large Language Model CG FedLLM
Making Text Embedders Few-Shot Learners
Chaofan Li, MingHao Qin, Shitao Xiao, Jianlyu Chen, Kun Luo, Yingxia Shao, Defu Lian, Zheng Liu
Large Language Model LeArning Abstract Text Embeddings Shot Example Input Context
Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation
Zheng Liu, Chenyuan Wu, Ninglu Shao, Shitao Xiao, Chaozhuo Li, Defu Lian
Large Language Model Retrieval Augmented Generation Question Answering Contextual Adapter
A Comprehensive Evaluation of Large Language Models on Mental Illnesses
Abdelrahman Hanafi, Mohammed Saad, Noureldin Zahran, Radwa J. Hanafy, Mohammed E. Fouda
Large Language Model Mental Health Model Performance Comprehensive Evaluation

September 23, 2024

SEAL: Suite for Evaluating API-use of LLMs
Woojeong Kim, Ashish Jagmohan, Aditya Vempaty
Large Language Model Real Time GPT 4 API Usage Software Suite Seal Generation
Enabling Resource-Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines
Lei Gao, Amir Ziashahabi, Yue Niu, Salman Avestimehr, Murali Annavaram
Large Language Model Efficient Fine Tuning Device Inference Zeroth Order Optimization Device Multi Modal Model Adaptation
A comprehensive study of on-device NLP applications -- VQA, automated Form filling, Smart Replies for Linguistic Codeswitching
Naman Goyal
Large Language Model User Response 3d Vqa Form Like Document Code Switching Device Use Case
CUTE: Measuring LLMs' Understanding of Their Tokens
Lukas Edman, Helmut Schmid, Alexander Fraser
Large Language Model Text Modality Human Understanding K TOKEN Happy Image
A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?
Yunfei Xie, Juncheng Wu, Haoqin Tu, Siwei Yang, Bingchen Zhao, Yongshuo Zong, Qiao Jin, Cihang Xie, Yuyin Zhou
Large Language Model Preliminary Study OpenAI Codex Real World Clinical Medical Question Answering Benchmark AI Doctor
AutoAPIEval: A Framework for Automated Evaluation of LLMs in API-Oriented Code Generation
Yixi Wu, Pengfei He, Zehao Wang, Shaowei Wang, Yuan Tian, Tse-Hsun (Peter)Chen
Large Language Model New Framework Code Generation Code Quality
Location is Key: Leveraging Large Language Model for Functional Bug Localization in Verilog
Bingkun Yao, Ning Wang, Jie Zhou, Xi Wang, Hong Gao, Zhe Jiang, Nan Guan
Large Language Model Location Information Verilog Code Generation Functional Verilog Description BUG Localization Verilog Textbook
Lessons Learned on Information Retrieval in Electronic Health Records: A Comparison of Embedding Models and Pooling Strategies
Skatje Myers, Timothy A. Miller, Yanjun Gao, Matthew M. Churpek, Anoop Mayampurath, Dmitriy Dligach, Majid Afshar
Large Language Model Retrieval Augmented Generation Consistent Comparison Information Retrieval Electronic Health Record Critical Lesson Clinical Text Retrieval Performance
Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling
Lechao Xiao
Large Language Model Machine Learning Strong Generalization Regularization Model Multiplicative Size Scaling Explicit Regularization Human Like Generalization
Boosting Healthcare LLMs Through Retrieved Context
Jordi Bayarri-Planas, Ashwin Kumar Gururajan, Dario Garcia-Gasulla
Large Language Model Natural Language Processing Choice Question Answering Context Retrieval
Evaluating the Usability of LLMs in Threat Intelligence Enrichment
Sanchana Srikanth, Mohammad Hasanuzzaman, Farah Tasnur Meem
Large Language Model Important Usability Challenge Threat Intelligence Attack Dataset
Scaling Laws of Decoder-Only Models on the Multilingual Machine Translation Task
Gaëtan Caillaut, Raheel Qader, Mariam Nakhlé, Jingshu Liu, Jean-Gabriel Barthélemy
Large Language Model Neural Machine Translation Scaling Law Translation Task Encoder Decoder Model Decoder Only Model
AlphaZip: Neural Network-Enhanced Lossless Text Compression
Swathi Shree Narashiman, Nitin Chandrachoodan
Large Language Model Neural Topic Data Compression Neural Compression Lossless Compression Compression Method

Large Language Model

Papers

Small Language Models: Survey, Measurements, and Insights

CHBench: A Chinese Dataset for Evaluating Health in Large Language Models

XTRUST: On the Multilingual Trustworthiness of Large Language Models

Federated Large Language Models: Current Progress and Future Directions

Making Text Embedders Few-Shot Learners

Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation

A Comprehensive Evaluation of Large Language Models on Mental Illnesses

SEAL: Suite for Evaluating API-use of LLMs

Enabling Resource-Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines

A comprehensive study of on-device NLP applications -- VQA, automated Form filling, Smart Replies for Linguistic Codeswitching

CUTE: Measuring LLMs' Understanding of Their Tokens

A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?

AutoAPIEval: A Framework for Automated Evaluation of LLMs in API-Oriented Code Generation

Location is Key: Leveraging Large Language Model for Functional Bug Localization in Verilog

Lessons Learned on Information Retrieval in Electronic Health Records: A Comparison of Embedding Models and Pooling Strategies

Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Boosting Healthcare LLMs Through Retrieved Context

Evaluating the Usability of LLMs in Threat Intelligence Enrichment

Scaling Laws of Decoder-Only Models on the Multilingual Machine Translation Task

AlphaZip: Neural Network-Enhanced Lossless Text Compression