Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Aligning Language Models to User Opinions
An important aspect of developing LLMs that interact with humans is to align models' behavior to their users. It is possible to prompt an LLM into behaving as a certain persona, especially a user…
OpenPI2.0: An Improved Dataset for Entity Tracking in Texts
Representing texts as information about entities has long been deemed effective in event reasoning. We propose OpenPI2.0, an improved dataset for tracking entity states in procedural texts.…
Just CHOP: Embarrassingly Simple LLM Compression
Large language models (LLMs) enable unparalleled few- and zero-shot reasoning capabilities but at a high computational footprint. A growing assortment of methods for compression promises to reduce…
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration
The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising. Existing works focus on coping with this concern while…
Improving Language Models via Plug-and-Play Retrieval Feedback
Large language models (LLMs) exhibit remarkable performance across various NLP tasks. However, they often generate incorrect or hallucinated information, which hinders their practical applicability…
Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery
Literature-Based Discovery (LBD) aims to discover new scientific knowledge by mining papers and generating hypotheses. Standard LBD is limited to predicting pairwise relations between discrete…
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…