Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
ADaPT: As-Needed Decomposition and Planning with Language Models
Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment. Recent works employ LLMs-as-agents in broadly two…
QualEval: Qualitative Evaluation for Model Improvement
Quantitative evaluation metrics have traditionally been pivotal in gauging the advancements of artificial intelligence systems, including large language models (LLMs). However, these metrics have…
Personalized Jargon Identification for Enhanced Interdisciplinary Communication
Scientific jargon can impede researchers when they read materials from other domains. Current methods of jargon identification mainly use corpus-level familiarity indicators (e.g., Simple Wikipedia…
NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge
Comparative knowledge (e.g., steel is stronger and heavier than styrofoam) is an essential component of our world knowledge, yet understudied in prior literature. In this paper, we harvest the…
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
Language technologies that accurately model the dynamics of events must perform commonsense reasoning. Existing work evaluating commonsense reasoning focuses on making inferences about common,…
JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models
The permanence of online content combined with the enhanced authorship identification techniques calls for stronger computational methods to protect the identity and privacy of online authorship…
MacGyver: Are Large Language Models Creative Problem Solvers?
We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting. To this end, we create MACGYVER, an automatically generated dataset consisting of over 1,600…
Impossible Distillation: from Low-Quality Model to High-Quality Dataset&Model for Summarization and Paraphrasing
We present Impossible Distillation, a novel framework for paraphrasing and sentence summarization, that distills a high-quality dataset and model from a low-quality teacher that itself cannot…
Promptly Predicting Structures: The Return of Inference
Prompt-based methods have been used extensively across NLP to build zero- and few-shot label predictors. Many NLP tasks are naturally structured: that is, their outputs consist of multiple labels…
On-the-fly Definition Augmentation of LLMs for Biomedical NER
Despite their general capabilities, LLMs still struggle on biomedical NER tasks, which are difficult due to the presence of specialized terminology and lack of training data. In this work we set out…