Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation
Coarse-grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse-grained cloud fields from a fine-grid reference model are a natural target for a…
PromptCap: Prompt-Guided Task-Aware Image Captioning
Knowledge-based visual question answering (VQA) involves questions that require world knowledge beyond the image to yield the correct answer. Large language models (LMs) like GPT-3 are particularly…
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
Despite thousands of researchers, engineers, and artists actively working on improving text-to-image generation models, systems often fail to produce images that accurately align with the text…
Exploiting Generalization in Offline Reinforcement Learning via Unseen State Augmentations
Offline reinforcement learning (RL) methods strike a balance between exploration and exploitation by conservative value estimation -- penalizing values of unseen states and actions. Model-free…
Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms
Bias evaluation benchmarks and dataset and model documentation have emerged as central processes for assessing the biases and harms of artificial intelligence (AI) systems. However, these auditing…
LEXPLAIN: Improving Model Explanations via Lexicon Supervision
Model explanations that shed light on the model’s predictions are becoming a desired additional output of NLP models, alongside their predictions. Challenges in creating these explanations include…
When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world…
Data-Efficient Finetuning Using Cross-Task Nearest Neighbors
Language models trained on massive prompted multitask datasets like T0 (Sanh et al., 2021) or FLAN (Wei et al., 2021a) can generalize to tasks unseen during training. We show that training on a…
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
Prompting-based large language models (LLMs) are surprisingly powerful at generating natural language reasoning steps or Chains-of-Thoughts (CoT) for multi-step question answering (QA). They…
DISCO: Distilling Phrasal Counterfactuals with Large Language Models
Recent methods demonstrate that data augmentation using counterfactual knowledge can teach models the causal structure of a task, leading to robust and generalizable models. However, such…