  Commonsense reasoning is one of the important aspect of natural language
understanding, with several benchmarks developed to evaluate it. However, only
a few of these benchmarks are available in languages other than English.
Developing parallel benchmarks facilitates cross-lingual evaluation, enabling a
better understanding of different languages. This research introduces a
collection of Winograd Schemas in Thai, a novel dataset designed to evaluate
commonsense reasoning capabilities in the context of the Thai language.
  Through a methodology involving native speakers, professional translators,
and thorough validation, the schemas aim to closely reflect Thai language
nuances, idioms, and cultural references while maintaining ambiguity and
commonsense challenges. We evaluate the performance of popular large language
models on this benchmark, revealing their strengths, limitations, and providing
insights into the current state-of-the-art. Results indicate that while models
like GPT-4 and Claude-3-Opus achieve high accuracy in English, their
performance significantly drops in Thai, highlighting the need for further
advancements in multilingual commonsense reasoning.
