LLMs vs. Torch 1.5: Why Your Code Assistant Can’t Keep Up

In the fast-evolving world of software libraries, code generation models are struggling to keep pace. Most existing benchmarks focus on static, version-agnostic code predictions, failing to capture the true complexity of adapting to frequent updates and maintaining compatibility with multiple library versions. To address this gap, we introduce GitChameleon, a novel dataset featuring 116 Python code completion tasks, each tied to specific library versions and accompanied by executable unit tests. This dataset is designed to rigorously evaluate the ability of large language models (LLMs) to generate version-specific code that is both syntactically correct and functionally accurate. Our findings are revealing: even state-of-the-art models like GPT-4o achieve a pass@10 of just 39.9% (43.7% with error feedback), highlighting significant limitations in their ability to adapt to versioned code. In this talk, I’ll explore why today’s LLMs, while impressive, still fall short in the dynamic landscape of evolving software libraries. By examining these challenges, we hope to spark a conversation about how to build more adaptable, reliable code generation tools for the future.

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