139 citations · 350 across the 16 of their papers we have counts for
47 papers
SocioProbe: What, When, and Where Language Models Learn about Sociodemographics
Anne Lauscher, Federico Bianchi, Samuel Bowman +1
Pre-trained language models (PLMs) have outperformed other NLP models on a wide range of tasks. Opting for a more thorough understanding of their capabilities and inner workings, r…
SQuALITY: Building a Long-Document Summarization Dataset the Hard Way
Alex Wang, Richard Yuanzhe Pang, Angelica Chen +2
Summarization datasets are often assembled either by scraping naturally occurring public-domain summaries -- which are nearly always in difficult-to-work-with technical domains --…
Instruction Induction: From Few Examples to Natural Language Task Descriptions
Or Honovich, Uri Shaham, Samuel R. Bowman +1
Large language models are able to perform a task by conditioning on a few input-output demonstrations - a paradigm known as in-context learning. We show that language models can ex…
Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers
Jason Phang, Haokun Liu, Samuel R. Bowman
Despite the success of fine-tuning pretrained language encoders like BERT for downstream natural language understanding (NLU) tasks, it is still poorly understood how neural networ…
Does Putting a Linguist in the Loop Improve NLU Data Collection?
Alicia Parrish, William Huang, Omar Agha +7
Many crowdsourced NLP datasets contain systematic gaps and biases that are identified only after data collection is complete. Identifying these issues from early data samples durin…
Efficient transfer learning for NLP with ELECTRA
François Mercier
Clark et al. [2020] claims that the ELECTRA approach is highly efficient in NLP performances relative to computation budget. As such, this reproducibility study focus on this claim…