97 citations · 102 across the 18 of their papers we have counts for
8 papers · 1 filter
Can They Dixit? Yes they Can! Dixit as a Playground for Multimodal Language Model Capabilities
Nishant Balepur, Dang Nguyen, Dayeon Ki
Multi-modal large language models (MLMs) are often assessed on static, individual benchmarks -- which cannot jointly assess MLM capabilities in a single task -- or rely on human or…
Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translations
Yimin Xiao, Yongle Zhang, Dayeon Ki +5
As Machine Translation (MT) becomes increasingly commonplace, understanding how the general public perceives and relies on imperfect MT is crucial for contextualizing MT research i…
Linguistic Nepotism: Trading-off Quality for Language Preference in Multilingual RAG
Dayeon Ki, Marine Carpuat, Paul McNamee +4
Multilingual Retrieval-Augmented Generation (mRAG) systems enable language models to answer knowledge-intensive queries with citation-supported responses across languages. Despite…
Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation
Dayeon Ki, Kevin Duh, Marine Carpuat
As people increasingly use AI systems in work and daily life, feedback mechanisms that help them use AI responsibly are urgently needed, particularly in settings where users are no…
Multiple LLM Agents Debate for Equitable Cultural Alignment
Dayeon Ki, Rachel Rudinger, Tianyi Zhou +1
Large Language Models (LLMs) need to adapt their predictions to diverse cultural contexts to benefit diverse communities across the world. While previous efforts have focused on si…
GraphicBench: A Planning Benchmark for Graphic Design with Language Agents
Dayeon Ki, Tianyi Zhou, Marine Carpuat +3
Large Language Model (LLM)-powered agents have unlocked new possibilities for automating human tasks. While prior work has focused on well-defined tasks with specified goals, the c…
AskQE: Question Answering as Automatic Evaluation for Machine Translation
Dayeon Ki, Kevin Duh, Marine Carpuat
How can a monolingual English speaker determine whether an automatic translation in French is good enough to be shared? Existing MT error detection and quality estimation (QE) tech…
Automatic Input Rewriting Improves Translation with Large Language Models
Dayeon Ki, Marine Carpuat
Can we improve machine translation (MT) with LLMs by rewriting their inputs automatically? Users commonly rely on the intuition that well-written text is easier to translate when u…