Commit0: Library Generation from Scratch
Keywords: code generation, language model, evaluation, feedback
TL;DR: We introduce Commit0, a novel benchmark designed to challenge AI agents to write libraries from scratch.
Abstract: With the goal of supporting research into AI that exceeds typical expert software development ability, we introduce Commit0, a benchmark that challenges AI agents to write libraries from scratch. Agents are provided with a specification document outlining the library's API as well as a suite of interactive unit tests, with the goal of producing an implementation of this API accordingly. The implementation is validated through running these unit tests. As a benchmark, Commit0 is designed to move beyond static one-shot code generation towards agents that must process long-form natural language specifications, adapt to multi-stage feedback, and generate code with complex dependencies. Commit0 also offers an interactive environment where models receive execution and linting feedback on the code they generate. Our experiments demonstrate that while current agents can pass some unit tests, none can yet fully reproduce full libraries. Results also show that interactive feedback is quite useful for models to generate code that passes more unit tests, validating the benchmarks that facilitate its use.
Primary Area: datasets and benchmarks
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Submission Number: 12832
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