84 citations · 655 across the 44 of their papers we have counts for
8 papers · 1 filter
Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters
Boshi Wang, Sewon Min, Xiang Deng +4
Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs). CoT explicitly encourages the LLM to generate intermed…
Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations
Xinxi Lyu, Sewon Min, Iz Beltagy +2
Although large language models can be prompted for both zero- and few-shot learning, performance drops significantly when no demonstrations are available. In this paper, we introdu…
Nonparametric Masked Language Modeling
Sewon Min, Weijia Shi, Mike Lewis +4
Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases. We introduce NPM, the first no…
CREPE: Open-Domain Question Answering with False Presuppositions
Xinyan Velocity Yu, Sewon Min, Luke Zettlemoyer +1
Information seeking users often pose questions with false presuppositions, especially when asking about unfamiliar topics. Most existing question answering (QA) datasets, in contra…
Exploring The Landscape of Distributional Robustness for Question Answering Models
Anas Awadalla, Mitchell Wortsman, Gabriel Ilharco +4
We conduct a large empirical evaluation to investigate the landscape of distributional robustness in question answering. Our investigation spans over 350 models and 16 question ans…
Measuring and Narrowing the Compositionality Gap in Language Models
Ofir Press, Muru Zhang, Sewon Min +3
We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We mea…