62 citations · 145 across the 23 of their papers we have counts for
6 papers · 1 filter
Impressions: Understanding Visual Semiotics and Aesthetic Impact
Julia Kruk, Caleb Ziems, Diyi Yang
Is aesthetic impact different from beauty? Is visual salience a reflection of its capacity for effective communication? We present Impressions, a novel dataset through which to inv…
CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation
Minzhi Li, Taiwei Shi, Caleb Ziems +4
Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (L…
Modeling Cross-Cultural Pragmatic Inference with Codenames Duet
Omar Shaikh, Caleb Ziems, William Held +3
Pragmatic reference enables efficient interpersonal communication. Prior work uses simple reference games to test models of pragmatic reasoning, often with unidentified speakers an…
TADA: Task-Agnostic Dialect Adapters for English
Will Held, Caleb Ziems, Diyi Yang
Large Language Models, the dominant starting point for Natural Language Processing (NLP) applications, fail at a higher rate for speakers of English dialects other than Standard Am…
NormBank: A Knowledge Bank of Situational Social Norms
Caleb Ziems, Jane Dwivedi-Yu, Yi-Chia Wang +2
We present NormBank, a knowledge bank of 155k situational norms. This resource is designed to ground flexible normative reasoning for interactive, assistive, and collaborative AI s…
Can Large Language Models Transform Computational Social Science?
Caleb Ziems, William Held, Omar Shaikh +3
Large Language Models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify a…