Functional programming languages differ fundamentally from imperative languages, posing a significant challenge for language models, such as CodeGPT and UniXcoder, that are fine-tuned primarily on the latter. A recent study delves into this issue, focusing on Haskell, and seeks to find ways to overcome the performance gap.

Summary

• Functional languages like Haskell are underrepresented in code completion models research.
• CodeGPT and UniXcoder are evaluated for Haskell code completion.
• An automatic evaluation reveals the need for better functional language representation in LLM pre-training.
• Manual evaluation shows frequent incomplete or incorrect predictions.

Key Points

• The study uses a publicly accessible Haskell dataset on HuggingFace for model fine-tuning.
• Results indicate that knowledge from imperative language models does not transfer well to functional languages.
• The HumanEval-Haskell dataset reveals that CodeGPT often generates empty or extra predictive comments.
• UniXcoder tends to produce incomplete or incorrect predictions more frequently.

This paper sheds light on the challenges faced by functional programming languages in the realm of AI-assisted code completion. The findings suggest a dire need for more high-quality Haskell datasets and indicate the potential for tailored models to improve functionality in this area.