Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Pace v0.2: a Python-based performance-portable atmospheric model
Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software…
Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
We study whether multiple large language models (LLMs) can autonomously improve each other in a negotiation game by playing, reflecting, and criticizing. We are interested in this question because…
LeTI: Learning to Generate from Textual Interactions
Finetuning pre-trained language models (LMs) enhances the models' capabilities. Prior techniques fine-tune a pre-trained LM on input-output pairs (e.g., instruction fine-tuning), or with numerical…
Machine-Learned Climate Model Corrections From a Global Storm-Resolving Model: Performance Across the Annual Cycle
One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned (ML) state-dependent corrections to the prognosed model tendencies, such that the climate model…
From Centralized to Ad-Hoc Knowledge Base Construction for Hypotheses Generation.
Objective To demonstrate and develop an approach enabling individual researchers or small teams to create their own ad-hoc, lightweight knowledge bases tailored for specialized scientific interests,…
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various domains with continuous-valued inputs. Despite the promises of fully non-autoregressive…
Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-Text Rationales
Among the remarkable emergent capabilities of large language models (LMs) is free-text rationalization; beyond a certain scale, large LMs are capable of generating seemingly useful rationalizations,…
Embedding Recycling for Language Models
Training and inference with large neural models is expensive. However, for many application domains, while new tasks and models arise frequently, the underlying doc-uments being modeled remain…
Decomposed Prompting: A Modular Approach for Solving Complex Tasks
Few-shot prompting is a surprisingly powerful way to use Large Language Models (LLMs) to solve various tasks. However, this approach struggles as the task complexity increases or when the individual…
Complexity-Based Prompting for Multi-Step Reasoning
We study the task of prompting large-scale language models to perform multi-step reasoning. Existing work shows that when prompted with a chain of thoughts (CoT), sequences of short sentences…