12 citations · 20 across the 13 of their papers we have counts for
14 papers · 1 filter
Evaluating Large Language Models for Antisemitic Incident Classification
Karina Halevy, Julia Mendelsohn, Chan Young Park +2
Addressing hate and violence in society requires timely detection of hateful events from public reporting, but automated identification of hateful events remains underexplored. We…
ComPO: Community Preferences for Language Model Personalization
Sachin Kumar, Chan Young Park, Yulia Tsvetkov +2
Conventional algorithms for training language models (LMs) with human feedback rely on preferences that are assumed to account for an "average" user, disregarding subjectivity and…
Locating Information Gaps and Narrative Inconsistencies Across Languages: A Case Study of LGBT People Portrayals on Wikipedia
Farhan Samir, Chan Young Park, Anjalie Field +2
To explain social phenomena and identify systematic biases, much research in computational social science focuses on comparative text analyses. These studies often rely on coarse c…
ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social Interactions
Chan Young Park, Shuyue Stella Li, Hayoung Jung +4
This study introduces ValueScope, a framework leveraging language models to quantify social norms and values within online communities, grounded in social science perspectives on n…
Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration
Shangbin Feng, Taylor Sorensen, Yuhan Liu +4
While existing alignment paradigms have been integral in developing large language models (LLMs), LLMs often learn an averaged human preference and struggle to model diverse prefer…
CulturalTeaming: AI-Assisted Interactive Red-Teaming for Challenging LLMs' (Lack of) Multicultural Knowledge
Yu Ying Chiu, Liwei Jiang, Maria Antoniak +7
Frontier large language models (LLMs) are developed by researchers and practitioners with skewed cultural backgrounds and on datasets with skewed sources. However, LLMs' (lack of)…