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.
Code- optimise: Self-generated preference data for correctness and efficiency.arXiv preprint arXiv:2406.12502
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 4representative citing papers
SkelDPO improves code generation efficiency by 2-7% over prior DPO methods via joint preference losses on full code and efficiency-critical skeletons.
InlineCoder reframes repository-level code generation as function-level coding by using a draft anchor to inline the target function into its call graph for upstream usage and downstream dependency context.
CROP uses compositional reasoning and expert preference alignment in VLMs to produce aesthetic crops that match human experts more closely than previous methods.
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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SkelDPO: A Skeleton-Guided Direct Preference Optimization Framework for Efficient Code Generation
SkelDPO improves code generation efficiency by 2-7% over prior DPO methods via joint preference losses on full code and efficiency-critical skeletons.
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In Line with Context: Repository-Level Code Generation via Context Inlining
InlineCoder reframes repository-level code generation as function-level coding by using a draft anchor to inline the target function into its call graph for upstream usage and downstream dependency context.
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CROP: Expert-Aligned Image Cropping via Compositional Reasoning and Optimizing Preference
CROP uses compositional reasoning and expert preference alignment in VLMs to produce aesthetic crops that match human experts more closely than previous methods.