2.3k citations · 2.3k across the 5 of their papers we have counts for
7 papers
A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents
Benjamin Newman, Luca Soldaini, Raymond Fok +2
Many real-world applications (e.g., note taking, search) require extracting a sentence or paragraph from a document and showing that snippet to a human outside of the source docume…
Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated Communication
Liye Fu, Benjamin Newman, Maurice Jakesch +1
Traditionally, writing assistance systems have focused on short or even single-word suggestions. Recently, large language models like GPT-3 have made it possible to generate signif…
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…
The EOS Decision and Length Extrapolation
Benjamin Newman, John Hewitt, Percy Liang +1
Extrapolation to unseen sequence lengths is a challenge for neural generative models of language. In this work, we characterize the effect on length extrapolation of a modeling dec…