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"

13 / 13 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 60 13 0 06 Jun 2024
What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability Mario Giulianelli Joris Baan Wilker Aziz Raquel Fernández Barbara Plank UQLM 54 31 0 19 May 2023
Examining the Inductive Bias of Neural Language Models with Artificial Languages Jennifer C. White Ryan Cotterell 65 44 0 02 Jun 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 450 2,093 0 31 Dec 2020
Evaluating Attribution Methods using White-Box LSTMs Sophie Hao FAtt XAI 51 8 0 16 Oct 2020
DeBERTa: Decoding-enhanced BERT with Disentangled Attention Pengcheng He Xiaodong Liu Jianfeng Gao Weizhu Chen AAML 151 2,731 0 05 Jun 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 602 4,801 0 23 Jan 2020
BLiMP: The Benchmark of Linguistic Minimal Pairs for English Alex Warstadt Alicia Parrish Haokun Liu Anhad Mohananey Wei Peng Sheng-Fu Wang Samuel R. Bowman 72 491 0 02 Dec 2019
RoBERTa: A Robustly Optimized BERT Pretraining Approach Yinhan Liu Myle Ott Naman Goyal Jingfei Du Mandar Joshi Danqi Chen Omer Levy M. Lewis Luke Zettlemoyer Veselin Stoyanov AIMat 633 24,431 0 26 Jul 2019
What Kind of Language Is Hard to Language-Model? Sabrina J. Mielke Ryan Cotterell Kyle Gorman Brian Roark Jason Eisner 89 78 0 11 Jun 2019
BERT Rediscovers the Classical NLP Pipeline Ian Tenney Dipanjan Das Ellie Pavlick MILM SSeg 135 1,471 0 15 May 2019
Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages Shauli Ravfogel Yoav Goldberg Tal Linzen 63 70 0 15 Mar 2019
What you can cram into a single vector: Probing sentence embeddings for linguistic properties Alexis Conneau Germán Kruszewski Guillaume Lample Loïc Barrault Marco Baroni 331 893 0 03 May 2018