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README.md

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   data_files:
   - split: test
     path: data/test-*
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+# Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
+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.
+
+The dataset’s dual natural language instructions test model efficiency in two scenarios: 
+1) Descriptive: Detailed instructions replicate situations where users provide specific specifications or
+another model outlines a plan, similar to Reflexion prompting,
+2) Lazy: Informal instructions resemble typical user queries
+for LLMs in code generation.
+
+For more information and results see [our paper](https://federico.codes/assets/papers/canitedit.pdf).
+
+## How To Evaluate
+All the code for evaluating the benchmark can be found in our [GitHub repository](https://github.com/nuprl/CanItEdit).