LLMEffiChecker: Understanding and Testing Efficiency Degradation of
Large Language Models

LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language Models

7 October 2022

Papers citing "LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language Models"

8 / 8 papers shown

Title
C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models Mintong Kang Nezihe Merve Gürel Ning Yu D. Song Bo-wen Li 76 20 0 05 Feb 2024
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation Jiawei Liu Chun Xia Yuyao Wang Lingming Zhang ELM ALM 178 780 0 02 May 2023
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions Minghao Wu Abdul Waheed Chiyu Zhang Muhammad Abdul-Mageed Alham Fikri Aji ALM 124 115 0 27 Apr 2023
DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks Simin Chen Mirazul Haque Cong Liu Wei Yang 36 21 0 10 Oct 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 301 11,730 0 04 Mar 2022
Multitask Prompted Training Enables Zero-Shot Task Generalization Victor Sanh Albert Webson Colin Raffel Stephen H. Bach Lintang Sutawika ... T. Bers Stella Biderman Leo Gao Thomas Wolf Alexander M. Rush LRM 203 1,651 0 15 Oct 2021
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang Weishi Wang Shafiq R. Joty S. Hoi 204 1,451 0 02 Sep 2021
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 948 20,214 0 17 Apr 2017