SwiftTrans improves both functional correctness and runtime efficiency of LLM code translations via multi-perspective exploration with hierarchical guidance and difference-aware selection with ordinal guidance on extended benchmarks including new SwiftBench.
Enhancing code translation in language models with few-shot learning via retrieval-augmented generation
4 Pith papers cite this work. Polarity classification is still indexing.
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Guided LLM strategies with custom datasets and execution-based verification enable functional APL-to-C# translation across a range of program complexities.
A large-scale study finds that many LLM code translation failures are false negatives due to improper evaluation configurations rather than incorrect translations.
NL specifications alone do not improve LLM code translation performance, but combining them with source code yields gains in select language pairs with no overall consistent benefit.
citing papers explorer
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Bridging Functional Correctness and Runtime Efficiency Gaps in LLM-Based Code Translation
SwiftTrans improves both functional correctness and runtime efficiency of LLM code translations via multi-perspective exploration with hierarchical guidance and difference-aware selection with ordinal guidance on extended benchmarks including new SwiftBench.
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Neural Code Translation of Legacy Code: APL to C#
Guided LLM strategies with custom datasets and execution-based verification enable functional APL-to-C# translation across a range of program complexities.
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Beyond Translation Accuracy: Addressing False Failures in LLM-Based Code Translation
A large-scale study finds that many LLM code translation failures are false negatives due to improper evaluation configurations rather than incorrect translations.
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Specification-Driven Code Translation Powered by Large Language Models: How Far Are We?
NL specifications alone do not improve LLM code translation performance, but combining them with source code yields gains in select language pairs with no overall consistent benefit.