LiveCodeBench collects 400 recent contest problems to create a contamination-free benchmark evaluating LLMs on code generation and related capabilities like self-repair and execution.
arXiv preprint arXiv:2303.07263 , year =
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Proposes autopoietic architectures for self-constructing software as a fundamental shift in the SDLC, leveraging foundation models for autonomous evolution and maintenance.
Smaller LLMs produce functional but limited Python code with variable quantization effects and quality/maintainability concerns that require validation before use.
citing papers explorer
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LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
LiveCodeBench collects 400 recent contest problems to create a contamination-free benchmark evaluating LLMs on code generation and related capabilities like self-repair and execution.
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Towards Enabling An Artificial Self-Construction Software Life-cycle via Autopoietic Architectures
Proposes autopoietic architectures for self-constructing software as a fundamental shift in the SDLC, leveraging foundation models for autonomous evolution and maintenance.
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Precision or Peril: A PoC of Python Code Quality from Quantized Large Language Models
Smaller LLMs produce functional but limited Python code with variable quantization effects and quality/maintainability concerns that require validation before use.