Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Phone2Proc: Bringing Robust Robots Into Our Chaotic World
Training embodied agents in simulation has become mainstream for the embodied AI community. However, these agents often struggle when deployed in the physical world due to their inability to…
Visual Programming: Compositional visual reasoning without training
We present VISPROG, a neuro-symbolic approach to solving complex and compositional visual tasks given natural language instructions. VISPROG avoids the need for any task-specific training. Instead,…
The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks
How reliably can we trust the scores obtained from social bias benchmarks as faithful indicators of problematic social biases in a given language model? In this work, we study this question by…
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion
We present LLM-Blender, an ensembling framework designed to attain consistently superior performance by leveraging the diverse strengths of multiple open-source large language models (LLMs). Our…
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved With Text
In-context vision and language models like Flamingo support arbitrarily interleaved sequences of images and text as input. This format not only enables few-shot learning via interleaving independent…
Evaluating the Social Impact of Generative AI Systems in Systems and Society
Generative AI systems across modalities, ranging from text, image, audio, and video, have broad social impacts, but there exists no official standard for means of evaluating those impacts and which…
Morphosyntactic probing of multilingual BERT models
We introduce an extensive dataset for multilingual probing of morphological information in language models (247 tasks across 42 languages from 10 families), each consisting of a sentence with a…
Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference
Pre-trained Transformer models like T5 and BART have advanced the state of the art on a wide range of text generation tasks. Compressing these models into smaller ones has become critically…
Commonsense Knowledge Transfer for Pre-trained Language Models
Despite serving as the foundation models for a wide range of NLP benchmarks, pre-trained language models have shown limited capabilities of acquiring implicit commonsense knowledge from…
EXCALIBUR: Encouraging and Evaluating Embodied Exploration
Experience precedes understanding. Humans constantly explore and learn about their environment out of curiosity, gather information, and update their models of the world. On the other hand,…