What they do when in doubt: a study of inductive biases in seq2seq learners

26 June 2020

Papers citing "What they do when in doubt: a study of inductive biases in seq2seq learners"

11 / 11 papers shown

Title
Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and Optimization Xinhao Yao Hongjin Qian Xiaolin Hu Gengze Xu Wei Liu Jian Luan Bin Wang Yue Liu 53 0 0 03 Oct 2024
Language acquisition: do children and language models follow similar learning stages? Linnea Evanson Yair Lakretz J. King 32 27 0 06 Jun 2023
Empirical Analysis of the Inductive Bias of Recurrent Neural Networks by Discrete Fourier Transform of Output Sequences Taiga Ishii Ryo Ueda Yusuke Miyao 24 0 0 16 May 2023
Injecting structural hints: Using language models to study inductive biases in language learning Isabel Papadimitriou Dan Jurafsky 25 13 0 25 Apr 2023
Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation Ronghao Dang Lu Chen Liuyi Wang Zongtao He Chengju Liu Qi Chen LRM 23 8 0 03 Feb 2023
Exploring Length Generalization in Large Language Models Cem Anil Yuhuai Wu Anders Andreassen Aitor Lewkowycz Vedant Misra V. Ramasesh Ambrose Slone Guy Gur-Ari Ethan Dyer Behnam Neyshabur ReLM LRM 38 160 0 11 Jul 2022
Discrete and continuous representations and processing in deep learning: Looking forward Ruben Cartuyvels Graham Spinks Marie-Francine Moens OCL 35 20 0 04 Jan 2022
The King is Naked: on the Notion of Robustness for Natural Language Processing Emanuele La Malfa Marta Z. Kwiatkowska 20 28 0 13 Dec 2021
How Do Neural Sequence Models Generalize? Local and Global Context Cues for Out-of-Distribution Prediction Anthony Bau Jacob Andreas 27 3 0 04 Nov 2021
Can Transformers Jump Around Right in Natural Language? Assessing Performance Transfer from SCAN Rahma Chaabouni Roberto Dessì Eugene Kharitonov 35 20 0 03 Jul 2021
On the proper role of linguistically-oriented deep net analysis in linguistic theorizing Marco Baroni 21 51 0 16 Jun 2021