2.3k citations · 2.3k across the 5 of their papers we have counts for
3 papers · 1 filter
P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts
Benjamin Newman, Prafulla Kumar Choubey, Nazneen Rajani
Recent work (e.g. LAMA (Petroni et al., 2019)) has found that the quality of the factual information extracted from Large Language Models (LLMs) depends on the prompts used to quer…
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.…
Refining Targeted Syntactic Evaluation of Language Models
Benjamin Newman, Kai-Siang Ang, Julia Gong +1
Targeted syntactic evaluation of subject-verb number agreement in English (TSE) evaluates language models' syntactic knowledge using hand-crafted minimal pairs of sentences that di…