10 citations · 14 across the 4 of their papers we have counts for
4 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…
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…
Assessing Phrasal Representation and Composition in Transformers
Lang Yu, Allyson Ettinger
Deep transformer models have pushed performance on NLP tasks to new limits, suggesting sophisticated treatment of complex linguistic inputs, such as phrases. However, we have limit…