Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distribution

23 October 2023

Papers citing "Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distribution"

7 / 7 papers shown

Title
What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages Nadav Borenstein Anej Svete R. Chan Josef Valvoda Franz Nowak Isabelle Augenstein Eleanor Chodroff Ryan Cotterell 38 9 0 06 Jun 2024
Do Transformers Parse while Predicting the Masked Word? Haoyu Zhao A. Panigrahi Rong Ge Sanjeev Arora 74 29 0 14 Mar 2023
"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification Jasmijn Bastings Sebastian Ebert Polina Zablotskaia Anders Sandholm Katja Filippova 107 75 0 14 Nov 2021
The paradox of the compositionality of natural language: a neural machine translation case study Verna Dankers Elia Bruni Dieuwke Hupkes CoGe 155 75 0 12 Aug 2021
The Pile: An 800GB Dataset of Diverse Text for Language Modeling Leo Gao Stella Biderman Sid Black Laurence Golding Travis Hoppe ... Horace He Anish Thite Noa Nabeshima Shawn Presser Connor Leahy AIMat 245 1,977 0 31 Dec 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 223 4,424 0 23 Jan 2020
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