Impact of Mixed-Domain Code-Text Fine-Tuning on JaCoText Generalization in Cross-Language Benchmarks

Abstract

Abstract: Pretrained transformer-based models have shown high performance in natural language generation task. However, a new wave of interest has surged: automatic programming language generation. This task consists of translating natural language instructions to a programming code. Despite the fact that well-known pretrained models on language generation have achieved good performance in learning programming languages, effort is still needed in automatic code generation. In this paper, we introduce JaCoText, a model based on Transformers neural network. It aims to generate java source code from natura

Research Question

What is the impact of incorporating mixed-domain (Java + other programming languages) code-text pairs during fine-tuning on JaCoText's generalization capability, as evaluated by pass@1 on cross-language benchmarks such as HumanEval-X?

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