2 citations · 5 across the 19 of their papers we have counts for
8 papers · 1 filter
M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAG
David Anugraha, Patrick Amadeus Irawan, Anshul Singh +2
Vision-language models (VLMs) have achieved strong performance in visual question answering (VQA), yet they remain constrained by static training data. Retrieval-Augmented Generati…
Rethinking what Matters: Effective and Robust Multilingual Realignment for Low-Resource Languages
Quang Phuoc Nguyen, David Anugraha, Felix Gaschi +2
Realignment is a promising strategy to improve cross-lingual transfer in multilingual language models. However, empirical results are mixed and often unreliable, particularly for t…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
mR3: Multilingual Rubric-Agnostic Reward Reasoning Models
David Anugraha, Shou-Yi Hung, Zilu Tang +3
Evaluation using Large Language Model (LLM) judges has been widely adopted in English and shown to be effective for automatic evaluation. However, their performance does not genera…
IndoPref: A Multi-Domain Pairwise Preference Dataset for Indonesian
Vanessa Rebecca Wiyono, David Anugraha, Ayu Purwarianti +1
Over 200 million people speak Indonesian, yet the language remains significantly underrepresented in preference-based research for large language models (LLMs). Most existing multi…
Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability
Genta Indra Winata, David Anugraha, Emmy Liu +17
High-quality datasets are fundamental to training and evaluating machine learning models, yet their creation-especially with accurate human annotations-remains a significant challe…
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…
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia
Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz +89
Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often resu…