Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization
📰 ArXiv cs.AI
Learn to improve code translation with syntax-guided and semantic-aware preference optimization using LLMs
Action Steps
- Apply syntax-guided preference optimization to LLMs for code translation
- Derive semantic rewards directly from source code
- Use preference-based learning to align code translation with semantic consistency
- Evaluate the effectiveness of syntax-guided and semantic-aware preference optimization on code translation tasks
- Integrate the proposed approach with existing code translation frameworks
Who Needs to Know This
This research benefits AI engineers and researchers working on code translation tasks, as it provides a novel approach to improving the accuracy and consistency of translated code
Key Insight
💡 Robust semantic rewards for code translation must be derived directly from the source code
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Improve code translation with syntax-guided & semantic-aware preference optimization!
Key Takeaways
Learn to improve code translation with syntax-guided and semantic-aware preference optimization using LLMs
Full Article
Title: Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization
Abstract:
arXiv:2605.13229v1 Announce Type: new Abstract: LLMs have shown immense potential for code translation, yet they often struggle to ensure both syntactic correctness and semantic consistency. While preference-based learning offers a promising alignment strategy, it is hindered by unreliable semantic rewards derived from sparse test cases or restrictive reference translations. We argue that a robust semantic reward for code translation must be derived directly from the source code. In this paper,
Abstract:
arXiv:2605.13229v1 Announce Type: new Abstract: LLMs have shown immense potential for code translation, yet they often struggle to ensure both syntactic correctness and semantic consistency. While preference-based learning offers a promising alignment strategy, it is hindered by unreliable semantic rewards derived from sparse test cases or restrictive reference translations. We argue that a robust semantic reward for code translation must be derived directly from the source code. In this paper,
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