A Preliminary Study of Multilingual Code Language Models for Code Generation Task Using Translated Benchmarks

TL;DR

This study evaluates multilingual code language models using translated benchmarks, revealing alignment with training metrics but also inconsistencies and reproducibility challenges, emphasizing the need for further empirical validation.

Contribution

It provides the first empirical assessment of translated benchmarks for multilingual code generation models, highlighting their potential and limitations.

Findings

Translated benchmarks align with training perplexity metrics.

Inconsistencies observed across different translated benchmarks.

Reproducibility challenges identified in performance evaluation.

Abstract

Evaluating the performance of Code Language Models (CLMs) for software engineering tasks, especially in multilingual and low-resource programming language settings, poses significant challenges. These challenges are primarily due to the lack of high-quality benchmarks across various programming languages and the imbalanced nature of the CLMs training corpus. Although recent advances in one of the common downstream tasks, code generation, have shown promise by introducing translated benchmarks using different methodologies, there is a current lack of empirical evidence assessing these benchmarks. To address this gap, we conducted a preliminary study to evaluate the performance of Poly-Coder, a pioneering open-source, multilingual CLM built for code generation. We utilized two existing state-of-the-art translations of the popular code generation benchmark, HumanEval, facilitated by the OctoPack and MultiPL-E studies. Our results suggest that the outcomes observed in these translated benchmarks align well with evaluation metrics used during the training phase, such as perplexity, thereby validating their effectiveness in estimating the performance of CLMs. However, we identified several inconsistencies in the CLMs' performance across the translated benchmarks and encountered challenges in replicating the results. These initial insights highlight the need for more comprehensive empirical studies to fully understand translated benchmarks' methodological approaches, limitations, and reproducibility. Such studies are essential to ensure their reliability before they are widely adopted.