FLeX: Fourier-based Low-rank EXpansion for multilingual transfer
arXiv:2604.06253v1 Announce Type: new Abstract: Cross-lingual code generation is critical in enterprise environments where multiple programming languages coexist. However, fine-tuning large language models (LLMs) individually for each language is computationally prohibitive. This paper investigates whether parameter-efficient fine-tuning methods and optimizer enhancements can improve cross-lingual transfer from Python to languages like Java. We fine-tune the Code Llama 7B model using low-rank adaptation (LoRA) to optimize a small subset of parameters and compare Adam and Sophia optimizers, while exploring a novel Fourier-based regularization technique. Our contributions include: (1)demonstrating that LoRA fine-tuning on a small, high-quality dataset (MBPP) can exceed the pass@1 performance of the more broadly fine-tuned Code Llama-Python-7B model (40.1% vs. 38.4%); (2) showing that while Sophia achieves faster convergence than Adam, final pass@1 scores show marginal differences; and (
arXiv:2604.06253v1 Announce Type: new Abstract: Cross-lingual code generation is critical in enterprise environments where multiple programming languages coexist. However, fine-tuning large language models (LLMs) individually for each language is computationally prohibitive. This paper investigates whether parameter-efficient fine-tuning methods and optimizer enhancements can improve cross-lingual transfer from Python to languages like Java. We fine-tune the Code Llama 7B model using low-rank adaptation (LoRA) to optimize a small subset of parameters and compare Adam and Sophia optimizers, while exploring a novel Fourier-based regularization technique. Our contributions include: (1)demonstrating that LoRA fine-tuning on a small, high-quality dataset (MBPP) can exceed the pass@1 performance of the more broadly fine-tuned Code Llama-Python-7B model (40.1% vs. 38.4%); (2) showing that while Sophia achieves faster convergence than Adam, final pass@1 scores show marginal differences; and (3) presenting evidence that Fourier-based regularization during fine-tuning significantly improves cross-lingual transfer, achieving 42.1% pass@1 on Java tasks compared to the 34.2% baseline. These findings suggest that combining LoRA, optimized training methods, and frequency-domain regularization can efficiently adapt single-language LLMs to perform well across multiple programming languages.
Executive Summary
The paper 'FLeX: Fourier-based Low-rank EXpansion for multilingual transfer' explores efficient cross-lingual adaptation of Large Language Models (LLMs) for code generation. Focusing on fine-tuning Code Llama 7B from Python to Java, the authors employ Low-Rank Adaptation (LoRA) and evaluate Adam and Sophia optimizers. A novel Fourier-based regularization technique is introduced, demonstrating significant improvements in cross-lingual transfer, achieving 42.1% pass@1 on Java tasks, surpassing even more broadly fine-tuned models. The study posits that combining LoRA, optimized training, and frequency-domain regularization offers a computationally efficient pathway to adapt single-language LLMs for robust multilingual performance, a critical finding for enterprise environments grappling with diverse programming language ecosystems.
Key Points
- ▸ LoRA fine-tuning on a small, high-quality dataset (MBPP) can outperform models fine-tuned on larger, broader datasets for specific language tasks.
- ▸ Sophia optimizer offers faster convergence than Adam, but final performance metrics (pass@1) show only marginal differences in this context.
- ▸ Fourier-based regularization during fine-tuning significantly enhances cross-lingual transfer, improving Java pass@1 performance from 34.2% to 42.1%.
- ▸ The combination of LoRA, optimized training methods, and frequency-domain regularization provides an efficient strategy for adapting single-language LLMs to multilingual code generation.
- ▸ The research addresses the computational burden of individual language model fine-tuning in enterprise settings with multiple programming languages.
Merits
Demonstrated Efficiency
The paper effectively shows that LoRA fine-tuning on a small, high-quality dataset can yield superior performance to broader fine-tuning, addressing a key computational challenge.
Novel Regularization Technique
The introduction and empirical validation of Fourier-based regularization as a significant enhancer for cross-lingual transfer is a notable methodological contribution.
Practical Relevance
The focus on cross-lingual code generation is highly relevant for enterprise environments, offering a tangible solution to a pervasive industry problem.
Clear Empirical Evidence
The results, particularly the pass@1 scores for Java, are clearly presented and demonstrate a substantial improvement attributable to the proposed methods.
Demerits
Limited Language Pairs
The study focuses primarily on Python-to-Java transfer. Generalizability across a wider spectrum of programming languages (e.g., C++, JavaScript, Go) remains to be fully explored.
Dataset Scope
While MBPP is high-quality, its relatively small size and specific problem domain might limit the generalizability of fine-tuned models to more diverse real-world coding challenges.
Optimizer Nuance
The 'marginal differences' between Adam and Sophia's final performance, despite Sophia's faster convergence, warrants deeper investigation into specific scenarios where one might decisively outperform the other.
Theoretical Underpinnings of Fourier Regularization
While effective, a more detailed theoretical exposition of *why* Fourier-based regularization aids cross-lingual transfer, perhaps linking it to signal processing analogies in language representations, would strengthen the contribution.
Expert Commentary
This paper presents a compelling argument for the efficacy of targeted, parameter-efficient fine-tuning combined with novel regularization in the challenging domain of cross-lingual code generation. The achievement of outperforming more broadly fine-tuned models with LoRA on a high-quality, smaller dataset is particularly noteworthy, suggesting a critical shift in how we approach LLM adaptation. The Fourier-based regularization is a genuinely innovative contribution, demonstrating a sophisticated understanding of how frequency-domain properties can be harnessed to improve model generalization. While the Python-to-Java focus is a strong start, a deeper exploration of its applicability across a broader spectrum of language families and paradigms (e.g., functional vs. object-oriented, static vs. dynamic typing) would solidify its universal relevance. Future work should also delve into the theoretical underpinnings of why frequency-domain regularization aids cross-lingual transfer, perhaps by analyzing feature representations, to provide a more comprehensive understanding beyond empirical success. This work sets a high bar for efficient LLM adaptation in enterprise settings.
Recommendations
- ✓ Conduct further experiments to validate FLeX across a wider and more diverse set of programming language pairs and families to assess its generalizability.
- ✓ Investigate the theoretical mechanisms by which Fourier-based regularization enhances cross-lingual transfer, potentially using interpretability techniques to analyze hidden state representations.
- ✓ Explore the impact of different dataset sizes and qualities on the effectiveness of LoRA and Fourier regularization for various target languages.
- ✓ Evaluate FLeX's performance on more complex and diverse code generation benchmarks beyond MBPP, including real-world enterprise coding tasks.
- ✓ Consider integrating FLeX with other advanced parameter-efficient fine-tuning (PEFT) methods (e.g., prompt tuning, prefix tuning) to explore synergistic effects.
Sources
Original: arXiv - cs.LG