97 citations · 102 across the 18 of their papers we have counts for
19 papers
Where's Waldo? Query-language Preference under Cross-lingual Knowledge Disparities
Dayeon Ki, Ruochen Zhang, Silviu Cucerzan +2
Large Language Models increasingly serve as interfaces for knowledge-intensive information seeking tasks across languages by synthesizing multilingual evidence. Prior work has show…
AdaMame: A Training Recipe for Adaptive Multilingual Reasoning
Dayeon Ki, Kevin Duh, Marine Carpuat
While Large Reasoning Models (LRMs) show strong performance in English, they often fail to reason in the language of the query, a phenomenon known as language collapse. Existing RL…
Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms
Dayeon Ki, Yu Hou, Rachel Rudinger +3
Neologisms and emerging slang are central to daily conversation, yet challenging for non-native speakers (NNS) to interpret and use appropriately in cross-cultural communication wi…
What Makes Good Multilingual Reasoning? Disentangling Traces with Measurable Features
Dayeon Ki, Kevin Duh, Marine Carpuat
Large Reasoning Models (LRMs) still exhibit large performance gaps between English and other languages, yet much current work assumes these gaps can be closed simply by making reas…
Pragmatics Meets Culture: Culturally-adapted Artwork Description Generation and Evaluation
Lingjun Zhao, Dayeon Ki, Marine Carpuat +1
Language models are known to exhibit various forms of cultural bias in decision-making tasks, yet much less is known about their degree of cultural familiarity in open-ended text g…
When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation
Mubashara Akhtar, Anka Reuel, Prajna Soni +34
Artificial intelligence benchmarks are an important mechanism to measure model progress and guide deployment decisions. However, benchmarks quickly "saturate", making it difficult…
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…
Mitigating Semantic Leakage in Cross-lingual Embeddings via Orthogonality Constraint
Dayeon Ki, Cheonbok Park, Hyunjoong Kim
Accurately aligning contextual representations in cross-lingual sentence embeddings is key for effective parallel data mining. A common strategy for achieving this alignment involv…
The Prompt Report: A Systematic Survey of Prompt Engineering Techniques
Sander Schulhoff, Michael Ilie, Nishant Balepur +28
Generative Artificial Intelligence (GenAI) systems are increasingly being deployed across diverse industries and research domains. Developers and end-users interact with these syst…
Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations
Dayeon Ki, Marine Carpuat
Machine Translation (MT) remains one of the last NLP tasks where large language models (LLMs) have not yet replaced dedicated supervised systems. This work exploits the complementa…
Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints
Yujin Baek, Koanho Lee, Dayeon Ki +3
Lexically-constrained NMT (LNMT) aims to incorporate user-provided terminology into translations. Despite its practical advantages, existing work has not evaluated LNMT models unde…
Cross-lingual QA: A Key to Unlocking In-context Cross-lingual Performance
Sunkyoung Kim, Dayeon Ki, Yireun Kim +1
Multilingual large language models (MLLMs) have demonstrated significant cross-lingual capabilities through in-context learning. Existing approaches typically construct monolingual…