Towards Understanding What Code Language Models Learned

Towards Understanding What Code Language Models Learned

20 June 2023

Dian Yu

Papers citing "Towards Understanding What Code Language Models Learned"

8 / 8 papers shown

Title
Do Current Language Models Support Code Intelligence for R Programming Language? Zixiao Zhao Fatemeh H. Fard ELM 35 0 0 10 Oct 2024
CodeGen2: Lessons for Training LLMs on Programming and Natural Languages Erik Nijkamp A. Ghobadzadeh Caiming Xiong Silvio Savarese Yingbo Zhou 144 163 0 03 May 2023
Redundancy and Concept Analysis for Code-trained Language Models Arushi Sharma Zefu Hu Christopher Quinn Ali Jannesari 42 1 0 01 May 2023
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
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Jason W. Wei Xuezhi Wang Dale Schuurmans Maarten Bosma Brian Ichter F. Xia Ed H. Chi Quoc Le Denny Zhou LM&Ro LRM AI4CE ReLM 315 8,261 0 28 Jan 2022
NOPE: A Corpus of Naturally-Occurring Presuppositions in English Alicia Parrish Sebastian Schuster Alex Warstadt Omar Agha Soo-hwan Lee Zhuoye Zhao Sam Bowman Tal Linzen LRM 28 23 0 14 Sep 2021
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation Shuai Lu Daya Guo Shuo Ren Junjie Huang Alexey Svyatkovskiy ... Nan Duan Neel Sundaresan Shao Kun Deng Shengyu Fu Shujie Liu ELM 190 853 0 09 Feb 2021
Semantic Robustness of Models of Source Code Goutham Ramakrishnan Jordan Henkel Zi Wang Aws Albarghouthi S. Jha Thomas W. Reps SILM AAML 35 95 0 07 Feb 2020