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path: data/test-*
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# Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
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CanItEdit is a benchmark for evaluating LLMs on instructional code editing, the task of updating a program given a natural language instruction. The benchmark contains 54 hand-crafted Python programs with before and after code blocks, two types of natural language instructions (descriptive and lazy), and a hidden test suite.
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The dataset’s dual natural language instructions test model efficiency in two scenarios:
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1) Descriptive: Detailed instructions replicate situations where users provide specific specifications or
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another model outlines a plan, similar to Reflexion prompting,
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2) Lazy: Informal instructions resemble typical user queries
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for LLMs in code generation.
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For more information and results see [our paper](https://federico.codes/assets/papers/canitedit.pdf).
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## How To Evaluate
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All the code for evaluating the benchmark can be found in our [GitHub repository](https://github.com/nuprl/CanItEdit).
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