Machine Reading, Fast and Slow: When Do Models "Understand" Language?

Machine Reading, Fast and Slow: When Do Models "Understand" Language?

15 September 2022

Sagnik Ray Choudhury

Isabelle Augenstein

Papers citing "Machine Reading, Fast and Slow: When Do Models "Understand" Language?"

12 / 12 papers shown

Title
Natural Language Processing RELIES on Linguistics Juri Opitz Shira Wein Nathan Schneider AI4CE 52 7 0 09 May 2024
Position: Key Claims in LLM Research Have a Long Tail of Footnotes Anna Rogers A. Luccioni 53 19 0 14 Aug 2023
Implicit causality in GPT-2: a case study H. Huynh T. Lentz Emiel van Miltenburg LRM 27 3 0 08 Dec 2022
The Debate Over Understanding in AI's Large Language Models Melanie Mitchell D. Krakauer ELM 74 203 0 14 Oct 2022
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining Andreas Madsen Nicholas Meade Vaibhav Adlakha Siva Reddy 103 35 0 15 Oct 2021
Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics Prajjwal Bhargava Aleksandr Drozd Anna Rogers 98 101 0 04 Oct 2021
Competency Problems: On Finding and Removing Artifacts in Language Data Matt Gardner William Merrill Jesse Dodge Matthew E. Peters Alexis Ross Sameer Singh Noah A. Smith 168 107 0 17 Apr 2021
To Test Machine Comprehension, Start by Defining Comprehension Jesse Dunietz Greg Burnham Akash Bharadwaj Owen Rambow Jennifer Chu-Carroll D. Ferrucci FaML 54 65 0 04 May 2020
How Can We Accelerate Progress Towards Human-like Linguistic Generalization? Tal Linzen 220 188 0 03 May 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 297 6,959 0 20 Apr 2018
Convolutional Neural Networks for Sentence Classification Yoon Kim AILaw VLM 255 13,364 0 25 Aug 2014
Efficient Estimation of Word Representations in Vector Space Tomáš Mikolov Kai Chen G. Corrado J. Dean 3DV 242 31,257 0 16 Jan 2013