← Search

Chan Young Park

17 accepted papers

2026

Spectrum Tuning: Post-Training for Distributional Coverage and In-Context Steerability

ICLR 2026poster

Language model post-training has enhanced instruction-following and performance on many downstream tasks, but also comes with an often-overlooked cost on tasks with many possible valid answers. We characterize three desiderata: in-context steerability, valid output space coverage, and distributional…

Cited by 0SourcecodeScholar
2025

ComPO: Community Preferences for Language Model Personalization

NAACL 2025long

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 finer-grained variations. Recent studies have raised concerns that aggregating such diverse and often contradictory huma…

2025

CulturalBench: A Robust, Diverse and Challenging Benchmark for Measuring LMs’ Cultural Knowledge Through Human-AI Red-Teaming

ACL 2025long

Robust, diverse, and challenging cultural knowledge benchmarks are essential for measuring our progress towards making LMs that are helpful across diverse cultures. We introduce CulturalBench: a set of 1,696 human-written and human-verified questions to assess LMs’ cultural knowledge, covering 45 gl…

Cited by 0SourcePDFScholar
2025

Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning

EMNLP 2025

Large language models (LLMs) have shown promise in robotic procedural planning, yet their human-centric reasoning often omits the low-level, grounded details needed for robotic execution. Vision-language models (VLMs) offer a path toward more perceptually grounded plans, but current methods either r

2025

Position: Political Neutrality in AI Is Impossible — But Here Is How to Approximate It

ICML 2025oral

AI systems often exhibit political bias, influencing users' opinions and decisions. While political neutrality—defined as the absence of bias—is often seen as an ideal solution for fairness and safety, this position paper argues that true political neutrality is neither feasible nor universally desi…

Cited by 0SourcePDFScholar
2025

SPICA: Retrieving Scenarios for Pluralistic In-Context Alignment

ACL 2025finding

When different groups’ values differ, one approach to model alignment is to steer models at inference time towards each group’s preferences. However, techniques like in-context learning only consider similarity when drawing few-shot examples and not cross-group differences in values. We propose SPIC…

2025

Subtle Risks, Critical Failures: A Framework for Diagnosing Physical Safety of LLMs for Embodied Decision Making

EMNLP 2025

Large Language Models (LLMs) are increasingly used for decision making in embodied agents, yet existing safety evaluations often rely on coarse success rates and domain-specific setups, making it difficult to diagnose why and where these models fail. This obscures our understanding of embodied safet

Cited by 0SourcePDFScholar
2024

Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptions

ICLR 2024poster

Language model (LM) prompting—a popular paradigm for solving NLP tasks—has been shown to be susceptible to miscalibration and brittleness to slight prompt variations, caused by its discriminative prompting approach, i.e., predicting the label given the input. To address these issues, we propose Gen-…

Cited by 1SourcePDFScholar
2024

Locating Information Gaps and Narrative Inconsistencies Across Languages: A Case Study of LGBT People Portrayals on Wikipedia

EMNLP 2024main

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 corpus-level statistics or local word-level analyses, mainly in English. We introduce the InfoGap method—an efficient and…

2024

Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration

EMNLP 2024main

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 preferences across cultures, demographics, and communities. We propose Modular Pluralism, a modular framework based on multi-L…

2024

P3Sum: Preserving Author’s Perspective in News Summarization with Diffusion Language Models

NAACL 2024long

In this work, we take a first step towards designing summarization systems that are faithful to the author’s intent, not only the semantic content of the article. Focusing on a case study of preserving political perspectives in news summarization, we find that existing approaches alter the political…

2024

ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social Interactions

EMNLP 2024finding

This study introduces ValueScope, a framework leveraging language models to quantify social norms and values within online communities, grounded in social science perspectives on normative structures. We employ ValueScope to dissect and analyze linguistic and stylistic expressions across 13 Reddit c…

2023

Analyzing Norm Violations in Live-Stream Chat

EMNLP 2023long main

Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and T…

Cited by 0SourceScholar
2023

From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models

ACL 2023long

Language models (LMs) are pretrained on diverse data sources—news, discussion forums, books, online encyclopedias. A significant portion of this data includes facts and opinions which, on one hand, celebrate democracy and diversity of ideas, and on the other hand are inherently socially biased. Our…

2023

TalkUp: Paving the Way for Understanding Empowering Language

EMNLP 2023long findings

Empowering language is important in many real-world contexts, from education to workplace dynamics to healthcare. Though language technologies are growing more prevalent in these contexts, empowerment has seldom been studied in NLP, and moreover, it is inherently challenging to operationalize becaus…

Cited by 0SourceScholar
2022

Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media

EMNLP 2022finding

NLP research on public opinion manipulation campaigns has primarily focused on detecting overt strategies such as fake news and disinformation. However, information manipulation in the ongoing Russia-Ukraine war exemplifies how governments and media also employ more nuanced strategies. We release a…

2021

Detecting Community Sensitive Norm Violations in Online Conversations

EMNLP 2021finding

Online platforms and communities establish their own norms that govern what behavior is acceptable within the community. Substantial effort in NLP has focused on identifying unacceptable behaviors and, recently, on forecasting them before they occur. However, these efforts have largely focused on to…