RoCode: A Dataset for Measuring Code Intelligence from Problem Definitions in Romanian
This addresses the problem of limited evaluation resources for code intelligence in non-English languages, particularly for researchers and developers working on Romanian or multilingual models, though it is incremental as it extends existing benchmarking efforts to a new language.
The authors tackled the lack of datasets for evaluating code-generation models in non-English languages by creating RoCode, a dataset of 2,642 competitive programming problems in Romanian with 11k solutions and testing suites, to benchmark and fine-tune models for Romanian or multilingual text.
Recently, large language models (LLMs) have become increasingly powerful and have become capable of solving a plethora of tasks through proper instructions in natural language. However, the vast majority of testing suites assume that the instructions are written in English, the de facto prompting language. Code intelligence and problem solving still remain a difficult task, even for the most advanced LLMs. Currently, there are no datasets to measure the generalization power for code-generation models in a language other than English. In this work, we present RoCode, a competitive programming dataset, consisting of 2,642 problems written in Romanian, 11k solutions in C, C++ and Python and comprehensive testing suites for each problem. The purpose of RoCode is to provide a benchmark for evaluating the code intelligence of language models trained on Romanian / multilingual text as well as a fine-tuning set for pretrained Romanian models. Through our results and review of related works, we argue for the need to develop code models for languages other than English.