AST-Probe: Recovering abstract syntax trees from hidden representations of pre-trained language models

23 June 2022

Papers citing "AST-Probe: Recovering abstract syntax trees from hidden representations of pre-trained language models"

7 / 7 papers shown

Title
Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit Yao Wan Yang He Zhangqian Bi Jianguo Zhang Hongyu Zhang Yulei Sui Guandong Xu Hai Jin Philip S. Yu 20 20 0 30 Dec 2023
The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification Anastasiia Grishina Max Hort Leon Moonen 19 6 0 08 May 2023
Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond Ensheng Shi Yanlin Wang Hongyu Zhang Lun Du Shi Han Dongmei Zhang Hongbin Sun 17 41 0 11 Apr 2023
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
Probing Classifiers: Promises, Shortcomings, and Advances Yonatan Belinkov 221 402 0 24 Feb 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
What you can cram into a single vector: Probing sentence embeddings for linguistic properties Alexis Conneau Germán Kruszewski Guillaume Lample Loïc Barrault Marco Baroni 199 876 0 03 May 2018