Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts
Text detoxification has the potential to miti- 001 gate the harms of toxicity by rephrasing text to 002 remove offensive meaning, but subtle toxicity 003 remains challenging to tackle. We introduce…
HINT: Hypernetwork Instruction Tuning for Efficient Few- and Zero-Shot Generalisation
Recent NLP models have shown the remarkable ability to effectively generalise `zero-shot' to new tasks using only natural language instructions as guidance. However, many of these approaches suffer…
From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models
Dogwhistles are coded expressions that simultaneously convey one meaning to a broad audience and a second one, often hateful or provocative, to a narrow in-group; they are deployed to evade both…
Reproducibility in NLP: What Have We Learned from the Checklist?
Scientific progress in NLP rests on the reproducibility of researchers' claims. The *CL conferences created the NLP Reproducibility Checklist in 2020 to be completed by authors at submission to…
NLPositionality: Characterizing Design Biases of Datasets and Models
Design biases in NLP systems, such as performance differences for different populations, often stem from their creator's positionality, i.e., views and lived experiences shaped by identity and…
COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements
Warning: This paper contains content that may be offensive or upsetting. Understanding the harms and offensiveness of statements requires reasoning about the social and situational context in which…
Do Androids Laugh at Electric Sheep? Humor"Understanding"Benchmarks from The New Yorker Caption Contest
We challenge AI models to “demonstrate un-derstanding” of the sophisticated multimodal humor of The New Yorker Caption Contest. Concretely, we develop three carefully cir-cumscribed tasks for which…
Do language models have coherent mental models of everyday things?
When people think of everyday things like an “egg,” they typically have a mental image associated with it. This commonsense knowledge helps us understand how these everyday things work and how to…
ClarifyDelphi: Reinforced Clarification Questions with Defeasibility Rewards for Social and Moral Situations
Context is everything, even in commonsense moral reasoning. Changing contexts can flip the moral judgment of an action; Lying to a friend is wrong in general, but may be morally acceptable if it is…
Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation
Few-shot fine-tuning and in-context learning are two alternative strategies for task adaptation of pre-trained language models. Recently, in-context learning has gained popularity over fine-tuning…