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Research - Papers

Explore a selection of our published work on a variety of key research challenges in AI.

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ADaPT: As-Needed Decomposition and Planning with Language Models

Archiki PrasadAlexander KollerMareike HartmannTushar Khot
2024
NAACL Findings

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

Vishvak MurahariAmeet DeshpandePeter ClarkAshwin Kalyan
2024
NAACL

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

Yue GuoJoseph Chee ChangMaria AntoniakTal August
2024
NAACL

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

Phillip HowardJunlin WangVasudev LalSwabha Swayamdipta
2024
NAACL

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

Wenting ZhaoJustin T ChiuJena D. HwangAlane Suhr
2024
NAACL

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

Jillian R. FisherXiming LuJaehun JungYejin Choi
2024
NAACL

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?

Yufei TianAbhilasha RavichanderLianhui QinFaeze Brahman
2024
NAACL

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

Jaehun JungPeter WestLiwei JiangYejin Choi
2024
NAACL

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

Maitrey MehtaValentina PyatkinVivek Srikumar
2024
NAACL

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

Monica MunnangiSergey FeldmanByron C WallaceAakanksha Naik
2024
NAACL 2024

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…