SWE-bench reveals that even top language models like Claude 2 resolve only 1.96% of 2,294 real-world GitHub issues, highlighting a gap in practical coding capabilities.
Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , pages=
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SynthFix adaptively routes LLM code repairs to supervised fine-tuning or symbolic-reward fine-tuning, yielding up to 32% higher exact match on JavaScript and C vulnerability benchmarks.
citing papers explorer
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SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
SWE-bench reveals that even top language models like Claude 2 resolve only 1.96% of 2,294 real-world GitHub issues, highlighting a gap in practical coding capabilities.
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SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair
SynthFix adaptively routes LLM code repairs to supervised fine-tuning or symbolic-reward fine-tuning, yielding up to 32% higher exact match on JavaScript and C vulnerability benchmarks.