2.3k citations · 2.3k across the 5 of their papers we have counts for
5 papers
Injecting structural hints: Using language models to study inductive biases in language learning
Isabel Papadimitriou, Dan Jurafsky
Both humans and large language models are able to learn language without explicit structural supervision. What inductive biases make this learning possible? We address this fundame…
Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models
Isabel Papadimitriou, Kezia Lopez, Dan Jurafsky
While multilingual language models can improve NLP performance on low-resource languages by leveraging higher-resource languages, they also reduce average performance on all langua…
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
William Chen, Siyi Hu, Rajat Talak +1
Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of a…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models
Isabel Papadimitriou, Dan Jurafsky
We propose transfer learning as a method for analyzing the encoding of grammatical structure in neural language models. We train LSTMs on non-linguistic data and evaluate their per…