arXiv:2605.27849v1 Announce Type: cross Abstract: Despite rapid progress in LLM-based code generation, existing models are predominantly trained on imperative languages, leaving functional programming languages (FPLs) such as Haskell, OCaml, and Scala chronically underexplored, with even frontier models performing substantially worse on FPLs. Fine-tuning is a natural remedy, but our experiments show that per-language fine-tuning fails to capture shared functional abstractions, while merged multi-language fine-tuning introduces cross-language interference. To address this, we introduce FPMoE, a
The rapid advancement in LLM-based code generation is now pushing researchers to address existing limitations, specifically concerning underrepresented programming paradigms like functional languages.
This development indicates a maturation in AI's ability to handle diverse programming languages, expanding the potential applications and improving accuracy in more specialized software development domains.
Current LLMs struggle with functional programming languages, but new approaches like FPMoE aim to overcome these limitations by better capturing shared functional abstractions without cross-language interference.
- · Functional programming developers
- · Companies using functional languages for critical systems
- · AI-assisted coding tool providers
- · Monolithic LLMs with poor FPL support
- · Developers reliant on manual FPL coding for complex tasks
FPMoE improves the performance of LLMs in generating functional programming language code.
Increased proficiency of AI in FPLs could lead to broader adoption of functional programming due to reduced development friction.
Enhanced AI capabilities in diverse programming paradigms might accelerate the development of more complex, reliable, and secure software systems, reducing manual coding errors across the industry.
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