Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion

23 January 2024

Papers citing "Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion"

8 / 8 papers shown

Title
Can Transformers Do Enumerative Geometry? Baran Hashemi Roderic G. Corominas Alessandro Giacchetto 32 2 0 27 Aug 2024
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small Kevin Wang Alexandre Variengien Arthur Conmy Buck Shlegeris Jacob Steinhardt 210 486 0 01 Nov 2022
On the Paradox of Learning to Reason from Data Honghua Zhang Liunian Harold Li Tao Meng Kai-Wei Chang Guy Van den Broeck NAI ReLM OOD LRM 132 102 0 23 May 2022
A Systematic Evaluation of Large Language Models of Code Frank F. Xu Uri Alon Graham Neubig Vincent J. Hellendoorn ELM ALM 196 624 0 26 Feb 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
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