5 citations · 9 across the 7 of their papers we have counts for
8 papers
R3: Robust Rubric-Agnostic Reward Models
David Anugraha, Zilu Tang, Lester James V. Miranda +5
Reward models are essential for aligning language model outputs with human preferences, yet existing approaches often lack both controllability and interpretability. These models a…
MetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference Calibration
David Anugraha, Garry Kuwanto, Lucky Susanto +2
We present MetaMetrics-MT, an innovative metric designed to evaluate machine translation (MT) tasks by aligning closely with human preferences through Bayesian optimization with Ga…
Linguistics Theory Meets LLM: Code-Switched Text Generation via Equivalence Constrained Large Language Models
Garry Kuwanto, Chaitanya Agarwal, Genta Indra Winata +1
Code-switching, the phenomenon of alternating between two or more languages in a single conversation, presents unique challenges for Natural Language Processing (NLP). Most existin…
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines
Genta Indra Winata, Frederikus Hudi, Patrick Amadeus Irawan +48
Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate th…
MetaMetrics: Calibrating Metrics For Generation Tasks Using Human Preferences
Genta Indra Winata, David Anugraha, Lucky Susanto +2
Understanding the quality of a performance evaluation metric is crucial for ensuring that model outputs align with human preferences. However, it remains unclear how well each metr…
Mitigating Translationese in Low-resource Languages: The Storyboard Approach
Garry Kuwanto, Eno-Abasi E. Urua, Priscilla Amondi Amuok +21
Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which can introduce the translationese effect…
DUnE: Dataset for Unified Editing
Afra Feyza Akyürek, Eric Pan, Garry Kuwanto +1
Even the most advanced language models remain susceptible to errors necessitating to modify these models without initiating a comprehensive retraining process. Model editing refers…
Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages
Garry Kuwanto, Afra Feyza Akyürek, Isidora Chara Tourni +3
We conduct an empirical study of neural machine translation (NMT) for truly low-resource languages, and propose a training curriculum fit for cases when both parallel training data…