16 citations · 29 across the 33 of their papers we have counts for
21 papers · 1 filter
Cross-Lingual Prompt Steerability: Towards Accurate and Robust LLM Behavior across Languages
Lechen Zhang, Yusheng Zhou, Tolga Ergen +3
System prompts provide a lightweight yet powerful mechanism for conditioning large language models (LLMs) at inference time. While prior work has focused on English-only settings,…
Digital Diasporas: How Origin Characteristics and Host-Native Distance Shape Immigrants' Online Cultural Retention
Aparup Khatua, David Jurgens, Ingmar Weber
Immigrants bring unique cultural backgrounds to their host countries. Subsequent interplay of cultures can lead to either a melting pot, where immigrants adopt the dominant culture…
Beyond the Explicit: A Bilingual Dataset for Dehumanization Detection in Social Media
Dennis Assenmacher, Paloma Piot, Katarina Laken +2
Digital dehumanization, although a critical issue, remains largely overlooked within the field of computational linguistics and Natural Language Processing. The prevailing approach…
Instruction Retrieval at Inference Time for Small Language Models
Kenan Alkiek, David Jurgens, Vinod Vydiswaran
The facts a language model stores are tied to its parameter count, so small models that fit on edge devices fail on expert problems, which need specialized knowledge and follow mul…
One Model, Many Morals: Uncovering Cross-Linguistic Misalignments in Computational Moral Reasoning
Sualeha Farid, Jayden Lin, Zean Chen +2
Large Language Models (LLMs) are increasingly deployed across multilingual and multicultural settings, yet it remains unclear whether changing language leads models to adopt commun…
Leveraging Multilingual Training for Authorship Representation: Enhancing Generalization across Languages and Domains
Junghwan Kim, Haotian Zhang, David Jurgens
Authorship representation (AR) learning, which models an author's unique writing style, has demonstrated strong performance in authorship attribution tasks. However, prior research…
Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives
Yinuo Xu, Veronica Derricks, Allison Earl +1
We present an approach to modeling annotator disagreement in subjective NLP tasks through both architectural and data-centric innovations. Our model, DEM-MoE (Demographic-Aware Mix…
NUTMEG: Separating Signal From Noise in Annotator Disagreement
Jonathan Ivey, Susan Gauch, David Jurgens
NLP models often rely on human-labeled data for training and evaluation. Many approaches crowdsource this data from a large number of annotators with varying skills, backgrounds, a…
Modeling Public Perceptions of Science in Media
Jiaxin Pei, Dustin Wright, Isabelle Augenstein +1
Effectively engaging the public with science is vital for fostering trust and understanding in our scientific community. Yet, with an ever-growing volume of information, science co…
Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework
Mohna Chakraborty, Lu Wang, David Jurgens
Large language models (LLMs) are increasingly deployed in domains requiring moral understanding, yet their reasoning often remains shallow, and misaligned with human reasoning. Unl…
The Impact of Generative AI on Social Media: An Experimental Study
Anders Giovanni Møller, Daniel M. Romero, David Jurgens +1
Generative Artificial Intelligence (AI) tools are increasingly deployed across social media platforms, yet their implications for user behavior and experience remain understudied,…
Are Economists Always More Introverted? Analyzing Consistency in Persona-Assigned LLMs
Manon Reusens, Bart Baesens, David Jurgens
Personalized Large Language Models (LLMs) are increasingly used in diverse applications, where they are assigned a specific persona - such as a happy high school teacher - to guide…
Evaluation Framework for AI Systems in "the Wild"
Sarah Jabbour, Trenton Chang, Anindya Das Antar +13
Generative AI (GenAI) models have become vital across industries, yet current evaluation methods have not adapted to their widespread use. Traditional evaluations often rely on ben…
Who Reaps All the Superchats? A Large-Scale Analysis of Income Inequality in Virtual YouTuber Livestreaming
Ruijing Zhao, Brian Diep, Jiaxin Pei +3
The explosive growth of Virtual YouTubers (VTubers)-streamers who perform behind virtual anime avatars-has created a unique digital economy with profound implications for content c…
Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions
Matthias Orlikowski, Jiaxin Pei, Paul Röttger +3
People naturally vary in their annotations for subjective questions and some of this variation is thought to be due to the person's sociodemographic characteristics. LLMs have also…
Neurobiber: Fast and Interpretable Stylistic Feature Extraction
Kenan Alkiek, Anna Wegmann, Jian Zhu +1
Linguistic style is pivotal for understanding how texts convey meaning and fulfill communicative purposes, yet extracting detailed stylistic features at scale remains challenging.…
Are Rules Meant to be Broken? Understanding Multilingual Moral Reasoning as a Computational Pipeline with UniMoral
Shivani Kumar, David Jurgens
Moral reasoning is a complex cognitive process shaped by individual experiences and cultural contexts and presents unique challenges for computational analysis. While natural langu…
The Noisy Path from Source to Citation: Measuring How Scholars Engage with Past Research
Hong Chen, Misha Teplitskiy, David Jurgens
Academic citations are widely used for evaluating research and tracing knowledge flows. Such uses typically rely on raw citation counts and neglect variability in citation types. I…
Tokenization is Sensitive to Language Variation
Anna Wegmann, Dong Nguyen, David Jurgens
Variation in language is ubiquitous and often systematically linked to regional, social, and contextual factors. Tokenizers split texts into smaller units and might behave differen…
Unstructured Evidence Attribution for Long Context Query Focused Summarization
Dustin Wright, Zain Muhammad Mujahid, Lu Wang +2
Large language models (LLMs) are capable of generating coherent summaries from very long contexts given a user query, and extracting and citing evidence spans helps improve the tru…
The Muddy Waters of Modeling Empathy in Language: The Practical Impacts of Theoretical Constructs
Allison Lahnala, Charles Welch, David Jurgens +1
Conceptual operationalizations of empathy in NLP are varied, with some having specific behaviors and properties, while others are more abstract. How these variations relate to one…