No Shortcuts to Culture: Indonesian Multi-hop Question Answering for Complex Cultural Understanding
Vynska Amalia Permadi, Xingwei Tan, Nafise Sadat Moosavi, Nikos Aletras

TL;DR
This paper introduces ID-MoCQA, a large-scale multi-hop question answering dataset focused on Indonesian cultural knowledge, designed to evaluate and improve the cultural reasoning capabilities of large language models.
Contribution
It presents the first multi-hop cultural QA dataset grounded in Indonesian traditions, with a novel framework for transforming single-hop questions into complex reasoning chains.
Findings
State-of-the-art models show significant gaps in cultural reasoning.
The dataset reveals challenges in nuanced cultural inference.
High-quality questions are ensured through expert review and LLM filtering.
Abstract
Understanding culture requires reasoning across context, tradition, and implicit social knowledge, far beyond recalling isolated facts. Yet most culturally focused question answering (QA) benchmarks rely on single-hop questions, which may allow models to exploit shallow cues rather than demonstrate genuine cultural reasoning. In this work, we introduce ID-MoCQA, the first large-scale multi-hop QA dataset for assessing the cultural understanding of large language models (LLMs), grounded in Indonesian traditions and available in both English and Indonesian. We present a new framework that systematically transforms single-hop cultural questions into multi-hop reasoning chains spanning six clue types (e.g., commonsense, temporal, geographical). Our multi-stage validation pipeline, combining expert review and LLM-as-a-judge filtering, ensures high-quality question-answer pairs. Our…
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Taxonomy
TopicsTopic Modeling · Multimodal Machine Learning Applications · Expert finding and Q&A systems
