Skip to main content ->
Ai2

Research - Papers

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

Filter papers

PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning

Faeze BrahmanChandra BhagavatulaValentina PyatkinYejin Choi
2024
ICLR

Procedural planning, which entails decomposing a high-level goal into a sequence of temporally ordered steps, is an important yet intricate task for machines. It involves integrating common-sense… 

Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting

Melanie SclarYejin ChoiYulia TsvetkovAlane Suhr
2024
ICLR

As large language models (LLMs) are adopted as a fundamental component of language technologies, it is crucial to accurately characterize their performance. Because choices in prompt design can… 

Tailoring Self-Rationalizers with Multi-Reward Distillation

Sahana RamnathBrihi JoshiSkyler HallinanXiang Ren
2024
ICLR

Large language models (LMs) are capable of generating free-text rationales to aid question answering. However, prior work 1) suggests that useful self-rationalization is emergent only at significant… 

The Generative AI Paradox: "What It Can Create, It May Not Understand"

Peter WestXiming LuNouha DziriYejin Choi
2024
ICLR

The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models now take only… 

The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning

Bill Yuchen LinAbhilasha RavichanderXiming LuYejin Choi
2024
ICLR

The alignment tuning process of large language models (LLMs) typically involves instruction learning through supervised fine-tuning (SFT) and preference tuning via reinforcement learning from human… 

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… 

CARE: Extracting Experimental Findings From Clinical Literature

Aakanksha NaikBailey KuehlErin BransomTom Hope
2024
NAACL 2024

Extracting fine-grained experimental findings from literature can provide dramatic utility for scientific applications. Prior work has developed annotation schemas and datasets for limited aspects… 

A Legal Risk Taxonomy for Generative Artificial Intelligence

David AtkinsonJacob Morrison
2024
arXiv.org

For the first time, this paper presents a taxonomy of legal risks associated with generative AI (GenAI) by breaking down complex legal concepts to provide a common understanding of potential legal… 

Estimating the Causal Effect of Early ArXiving on Paper Acceptance

Yanai ElazarJiayao ZhangDavid WaddenNoah A. Smith
2024
CLearR

What is the effect of releasing a preprint of a paper before it is submitted for peer review? No randomized controlled trial has been conducted, so we turn to observational data to answer this… 

The precipitation response to warming and CO2 increase: A comparison of a global storm resolving model and CMIP6 models.

Ilai GuendelmanTimothy M. MerlisKai-Yuan ChengStephan Fueglistaler
2024
Geophysical Research Letters

Global storm-resolving models (GSRMs) can explicitly resolve some of deep convection are now being integrated for climate timescales. GSRMs are able to simulate more realistic precipitation…