INCLUDE: Evaluating Multilingual Language Understanding with Regional
  Knowledge
The performance differential of large language models (LLM) between languages
hinders their effective deployment in many regions, inhibiting the potential
economic and societal value of generative AI tools in many communities.
However, the development of functional LLMs in many languages (\ie,
multilingual LLMs) is bottlenecked by the lack of high-quality evaluation
resources in languages other than English. Moreover, current practices in
multilingual benchmark construction often translate English resources, ignoring
the regional and cultural knowledge of the environments in which multilingual
systems would be used. In this work, we construct an evaluation suite of
197,243 QA pairs from local exam sources to measure the capabilities of
multilingual LLMs in a variety of regional contexts. Our novel resource,
INCLUDE, is a comprehensive knowledge- and reasoning-centric benchmark across
44 written languages that evaluates multilingual LLMs for performance in the
actual language environments where they would be deployed.
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INCLUDE: Evaluating Multilingual Language Understanding with Regional
  Knowledge
The performance differential of large language models (LLM) between languages
hinders their effective deployment in many regions, inhibiting the potential
economic and societal value of generative AI tools in many communities.
However, the development of functional LLMs in many languages (\ie,
multilingual LLMs) is bottlenecked by the lack of high-quality evaluation
resources in languages other than English. Moreover, current practices in
multilingual benchmark construction often translate English resources, ignoring
the regional and cultural knowledge of the environments in which multilingual
systems would be used. In this work, we construct an evaluation suite of
197,243 QA pairs from local exam sources to measure the capabilities of
multilingual LLMs in a variety of regional contexts. Our novel resource,
INCLUDE, is a comprehensive knowledge- and reasoning-centric benchmark across
44 written languages that evaluates multilingual LLMs for performance in the
actual language environments where they would be deployed.
INCLUDE, a novel evaluation suite with QA pairs from local exam sources, measures multilingual LLM performance across 44 languages for real-world deployment.
large language models multilingual LLMs evaluation resources multilingual benchmark regional contexts knowledge-centric benchmark reasoning-centric benchmark