10 citations · 18 across the 10 of their papers we have counts for
16 papers · 1 filter
Counterfactual reasoning: Do language models need world knowledge for causal understanding?
Jiaxuan Li, Lang Yu, Allyson Ettinger
Current pre-trained language models have enabled remarkable improvements in downstream tasks, but it remains difficult to distinguish effects of statistical correlation from more s…
"No, they did not": Dialogue response dynamics in pre-trained language models
Sanghee J. Kim, Lang Yu, Allyson Ettinger
A critical component of competence in language is being able to identify relevant components of an utterance and reply appropriately. In this paper we examine the extent of such di…
A Property Induction Framework for Neural Language Models
Kanishka Misra, Julia Taylor Rayz, Allyson Ettinger
To what extent can experience from language contribute to our conceptual knowledge? Computational explorations of this question have shed light on the ability of powerful neural la…
Pragmatic competence of pre-trained language models through the lens of discourse connectives
Lalchand Pandia, Yan Cong, Allyson Ettinger
As pre-trained language models (LMs) continue to dominate NLP, it is increasingly important that we understand the depth of language capabilities in these models. In this paper, we…
Sorting through the noise: Testing robustness of information processing in pre-trained language models
Lalchand Pandia, Allyson Ettinger
Pre-trained LMs have shown impressive performance on downstream NLP tasks, but we have yet to establish a clear understanding of their sophistication when it comes to processing, r…
On the Interplay Between Fine-tuning and Composition in Transformers
Lang Yu, Allyson Ettinger
Pre-trained transformer language models have shown remarkable performance on a variety of NLP tasks. However, recent research has suggested that phrase-level representations in the…