Ben Newman

blnewman at stanford.edu

Github | CV | Google Scholar | Blog

Hello! I'm Ben Newman, currently a Computer Science MS student at Stanford working with the Stanford NLP group. I'm interested in understanding how NLP systems learn and process human language and the role language technologies can play in society more broadly.

I've worked on projects analyzing models' abilities to extrapolate, process syntax, and communicate. I've also thought about how language technologies can usefully augment human language abilities, both for scientific discovery (e.g. testing psycholinguistic hypotheses) and in political science (e.g. countering minsinformation). I've been a course assistant for two of Stanford's NLP classes, CS124 and CS224N, and have co-taught courses in Introductory Linguistics and Computing Fundamentals to high schoolers at Stanford Splash.

Publications

P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts

Benjamin Newman, Prafulla Kumar Choubey, Nazneen Rajani

ICLR 2022 (Poster)

[pdf][tldr]

Refining Targeted Syntactic Evaluation

Benjamin Newman, Kai Siang-Ang, Julia Gong, John Hewitt

NAACL 2021

[pdf] [code] [cite] [blog] [tldr]

The EOS Decision and Length Extrapolation

Benjamin Newman, John Hewitt, Percy Liang and Chris Manning

Blackbox NLP@EMNLP 2020 (Outstanding Paper)

[pdf] [code] [cite] [tldr]

Communication-based Evaluation for Natural Language Generation

Benjamin Newman, Reuben Cohn-Gordon, and Christopher Potts

Society for Computation in Linguistics@LSA 2020

[pdf] [code] [cite] [tldr]

Large-Scale Collaborations

On the Opportunities and Risks of Foundation Models

Bommasani et al., 2021

Misuse Section: Antoine Bousselut*, Shelby Grossman*, and Benjamin Newman [pdf]

The Long Fuse: Misinformation and the 2020 Election

The Election Integrity Partnership was a coalition of research groups that tracked misinformation in the run-up to the 2020 US election. [site][pdf]

Course Projects

Unsupervised Recovery of Tree Metrics from Learned Representations

Representations from pretrained language models likely incorporate syntax, but can we access it without training supervised probes? [pdf]

CS229: Machine Learning. Final Project (2019).

English-Chinese Name Machine Transliteration Using Search and Neural Networks

What's your name in Chinese? Translating a name is different from translating an article because name translations are based in phoenetics and lack large corpera. We explore two approaches. [pdf] [code]

CS221: Artificial Intelligence: Principles and Techniques: Final Project with Julia Gong (2018).

Using POMDPs to Learn Language in a Spatial Reference Game

How can you teach computational agents to follow directions without defining what each instruction means? POMDPs! [pdf] [code]

CS238: Decision Making under Uncertainty: Final Project with Suvir Mirchandani and Levi Lian (2018)

Swear Analysis

What we can learn about people's use of swears by looking at their word2vec and GLOVE embeddings? [pdf]

Linguist 130A: Semantics and Pragmatics: Final Project with Julia Gong (2018)

Zero Width Space Encrypter

Hiding secret messages in HTML zero-width space characters. Demo here!

Stanford Student Space Initiative (SSI)

Class Notes

Stanford Splash Slides: In a Manner of Speaking...

LINGUIST 21N: Linguistic Diversity and Universals