1 citations · 1 across the 8 of their papers we have counts for
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NOLLI: A Difficulty-Calibrated Puzzle Benchmark for Diagnosing the English-Korean Performance Gap
Dasol Choi, Joonyong Park, Daegon Yu +3
We introduce NOLLI, a procedurally generated English-Korean puzzle benchmark designed to diagnose where Korean performance gaps arise. It comprises 15 puzzle types (25 tasks; 7,500…
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…
Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thought
Guijin Son, Donghun Yang, Hitesh Laxmichand Patel +9
Recent frontier models employ long chain-of-thought reasoning to explore solution spaces in context and achieve stonger performance. While many works study distillation to build sm…
Ko-PIQA: A Korean Physical Commonsense Reasoning Dataset with Cultural Context
Dasol Choi, Jungwhan Kim, Guijin Son
Physical commonsense reasoning datasets like PIQA are predominantly English-centric and lack cultural diversity. We introduce Ko-PIQA, a Korean physical commonsense reasoning datas…
Multi-Step Reasoning in Korean and the Emergent Mirage
Guijin Son, Hyunwoo Ko, Dasol Choi
We introduce HRMCR (HAE-RAE Multi-Step Commonsense Reasoning), a benchmark designed to evaluate large language models' ability to perform multi-step reasoning in culturally specifi…
Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap
Hyunwoo Ko, Guijin Son, Dasol Choi
Large language models (LLMs) demonstrate exceptional performance on complex reasoning tasks. However, despite their strong reasoning capabilities in high-resource languages (e.g.,…