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20192026
most citedInverse Scaling: When Bigger Isn't Better

24 citations · 62 across the 17 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.CL2022

Language model acceptability judgements are not always robust to context

Koustuv Sinha, Jon Gauthier, Aaron Mueller +4

Targeted syntactic evaluations of language models ask whether models show stable preferences for syntactically acceptable content over minimal-pair unacceptable inputs. Most target…

cs.CL2022

Causal Analysis of Syntactic Agreement Neurons in Multilingual Language Models

Aaron Mueller, Yu Xia, Tal Linzen

Structural probing work has found evidence for latent syntactic information in pre-trained language models. However, much of this analysis has focused on monolingual models, and an…

cs.CL2022★ 8 cited

What Do NLP Researchers Believe? Results of the NLP Community Metasurvey

Julian Michael, Ari Holtzman, Alicia Parrish +8

We present the results of the NLP Community Metasurvey. Run from May to June 2022, the survey elicited opinions on controversial issues, including industry influence in the field,…

cs.CL2022★ 1 cited

Label Semantic Aware Pre-training for Few-shot Text Classification

Aaron Mueller, Jason Krone, Salvatore Romeo +4

In text classification tasks, useful information is encoded in the label names. Label semantic aware systems have leveraged this information for improved text classification perfor…

cs.CL2022★ 1 cited

Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models

Aaron Mueller, Robert Frank, Tal Linzen +2

Relations between words are governed by hierarchical structure rather than linear ordering. Sequence-to-sequence (seq2seq) models, despite their success in downstream NLP applicati…