Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker
Theory of Mind (ToM)$\unicode{x2014}$the ability to reason about the mental states of other people$\unicode{x2014}$is a key element of our social intelligence. Yet, despite their ever more…
LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization
While human evaluation remains best practice for accurately judging the faithfulness of automatically-generated summaries, few solutions exist to address the increased difficulty and workload when…
CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context
When reading a scholarly article, inline citations help researchers contextualize the current article and discover relevant prior work. However, it can be challenging to prioritize and make sense of…
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community's programs over…
Abstract Visual Reasoning with Tangram Shapes
We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines. Drawing on the history of tangram puzzles as stimuli in cognitive science, we build a richly…
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation
The full power of human language-based communication cannot be realized without negation. All human languages have some form of negation. Despite this, negation remains a challenging phenomenon for…
ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
Massive datasets and high-capacity models have driven many recent advancements in computer vision and natural language understanding. This work presents a platform to enable similar success stories…
Robust fine-tuning of zero-shot models
Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset).…
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however,…
Understanding Dataset Difficulty with 𝒱-Usable Information
Estimating the difficulty of a dataset typically involves comparing state-of-the-art models to humans; the bigger the performance gap, the harder the dataset is said to be. However, this comparison…