Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Harmonic Mobile Manipulation
Recent advancements in robotics have enabled robots to navigate complex scenes or manipulate diverse objects independently. However, robots are still impotent in many household tasks requiring…
IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions
Although counterfactual reasoning is a fundamental aspect of intelligence, the lack of large-scale counterfactual open-domain question-answering (QA) benchmarks makes it difficult to evaluate and…
Probabilistic Precipitation Downscaling with Optical Flow-Guided Diffusion
In climate science and meteorology, local precipitation predictions are limited by the immense computational costs induced by the high spatial resolution that simulation methods require. A common…
Self-Refine: Iterative Refinement with Self-Feedback
Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for…
A Logic for Expressing Log-Precision Transformers
One way to interpret the reasoning power of transformer-based language models is to describe the types of logical rules they can resolve over some input text. Recently, Chiang et al. (2023) showed…
Faith and Fate: Limits of Transformers on Compositionality
Transformer large language models (LLMs) have sparked admiration for their exceptional performance on tasks that demand intricate multi-step reasoning. Yet, these models simultaneously show failures…
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
Language models (LMs) often exhibit undesirable text generation behaviors, including generating false, toxic, or irrelevant outputs. Reinforcement learning from human feedback (RLHF) - where human…
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
In this work we explore recent advances in instruction-tuning language models on a range of open instruction-following datasets. Despite recent claims that open models can be on par with…
RealTime QA: What's the Answer Right Now?
We introduce R EAL T IME QA, a dynamic question answering (QA) platform that announces questions and evaluates systems on a regular basis (weekly in this version). R E AL T IME QA inquires about the…
SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality
In the last year alone, a surge of new benchmarks to measure compositional understanding of vision-language models have permeated the machine learning ecosystem. Given an image, these benchmarks…