FPMoE: A Sparse Mixture-of-Experts Approach to Functional Code Generation
Learn how FPMoE, a sparse mixture-of-experts approach, improves functional code generation in programming languages like Haskell and OCaml, and why it matters for advancing LLM-based code generation
- Implement FPMoE using a sparse mixture-of-experts architecture to generate functional code
- Fine-tune the model on a dataset of functional programming languages to capture language-specific features
- Evaluate the performance of FPMoE on a benchmark of functional code generation tasks
- Compare the results with existing models to assess the improvement in performance
- Apply FPMoE to other programming languages to explore its generalizability
Researchers and developers working on LLM-based code generation, particularly those interested in functional programming languages, can benefit from this approach to improve model performance and capture shared functional abstractions
💡 FPMoE captures shared functional abstractions across languages, outperforming per-language fine-tuning and existing models
💡 FPMoE: A sparse mixture-of-experts approach for functional code generation, improving performance on languages like Haskell and OCaml #LLM #CodeGeneration #FunctionalProgramming
Key Takeaways
Learn how FPMoE, a sparse mixture-of-experts approach, improves functional code generation in programming languages like Haskell and OCaml, and why it matters for advancing LLM-based code generation
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Abstract:
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
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