165 citations · 502 across the 11 of their papers we have counts for
11 papers
CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees
S M Rafiuddin, Atriya Sen
Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…
Gemma 2: Improving Open Language Models at a Practical Size
Gemma Team, Morgane Riviere, Shreya Pathak +195
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In th…
Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers
Daphne Ippolito, Ann Yuan, Andy Coenen +1
Recent developments in natural language generation (NLG) using neural language models have brought us closer than ever to the goal of building AI-powered creative writing tools. Ho…
LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia
Steven M. Goodman, Erin Buehler, Patrick Clary +15
Prior work has explored the writing challenges experienced by people with dyslexia, and the potential for new spelling, grammar, and word retrieval technologies to address these ch…
The Case for a Single Model that can Both Generate Continuations and Fill in the Blank
Daphne Ippolito, Liam Dugan, Emily Reif +3
The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural…
SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasets
Ann Yuan, Daphne Ippolito, Vitaly Nikolaev +3
NLP researchers need more, higher-quality text datasets. Human-labeled datasets are expensive to collect, while datasets collected via automatic retrieval from the web such as Wiki…
A Recipe For Arbitrary Text Style Transfer with Large Language Models
Emily Reif, Daphne Ippolito, Ann Yuan +3
In this paper, we leverage large language models (LMs) to perform zero-shot text style transfer. We present a prompting method that we call augmented zero-shot learning, which fram…
Wordcraft: a Human-AI Collaborative Editor for Story Writing
Andy Coenen, Luke Davis, Daphne Ippolito +2
As neural language models grow in effectiveness, they are increasingly being applied in real-world settings. However these applications tend to be limited in the modes of interacti…
An Interpretability Illusion for BERT
Tolga Bolukbasi, Adam Pearce, Ann Yuan +4
We describe an "interpretability illusion" that arises when analyzing the BERT model. Activations of individual neurons in the network may spuriously appear to encode a single, sim…
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models
Ian Tenney, James Wexler, Jasmijn Bastings +8
We present the Language Interpretability Tool (LIT), an open-source platform for visualization and understanding of NLP models. We focus on core questions about model behavior: Why…
Visualizing and Measuring the Geometry of BERT
Andy Coenen, Emily Reif, Ann Yuan +4
Transformer architectures show significant promise for natural language processing. Given that a single pretrained model can be fine-tuned to perform well on many different tasks,…