Sorting through the noise: Testing robustness of information processing in pre-trained language models

25 September 2021

Papers citing "Sorting through the noise: Testing robustness of information processing in pre-trained language models"

9 / 9 papers shown

Title
ACCORD: Closing the Commonsense Measurability Gap François Roewer-Després Jinyue Feng Zining Zhu Frank Rudzicz LRM 48 0 0 04 Jun 2024
Trustworthy Formal Natural Language Specifications Colin S. Gordon Sergey Matskevich HILM 27 3 0 05 Oct 2023
Large Language Models Can Be Easily Distracted by Irrelevant Context Freda Shi Xinyun Chen Kanishka Misra Nathan Scales David Dohan Ed H. Chi Nathanael Scharli Denny Zhou ReLM RALM LRM 30 529 0 31 Jan 2023
Transparency Helps Reveal When Language Models Learn Meaning Zhaofeng Wu William Merrill Hao Peng Iz Beltagy Noah A. Smith 19 9 0 14 Oct 2022
Natural Language Specifications in Proof Assistants Colin S. Gordon Sergey Matskevich 33 1 0 16 May 2022
When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it Sebastian Schuster Tal Linzen 13 25 0 06 May 2022
Measuring and Improving Consistency in Pretrained Language Models Yanai Elazar Nora Kassner Shauli Ravfogel Abhilasha Ravichander Eduard H. Hovy Hinrich Schütze Yoav Goldberg HILM 263 346 0 01 Feb 2021
Spying on your neighbors: Fine-grained probing of contextual embeddings for information about surrounding words Josef Klafka Allyson Ettinger 51 42 0 04 May 2020
Language Models as Knowledge Bases? Fabio Petroni Tim Rocktaschel Patrick Lewis A. Bakhtin Yuxiang Wu Alexander H. Miller Sebastian Riedel KELM AI4MH 415 2,586 0 03 Sep 2019