ICRAG, a retrieval-augmented iterative code-generation framework, improves accuracy on 13 NLP benchmarks by compiling questions into Python programs and executing them, though gains are partly inflated by in-distribution retrieval.
In: Proceedings of the 40th International Conference on Machine Learning
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Understanding Benchmark Language Under Weakened Formal Semantics
ICRAG, a retrieval-augmented iterative code-generation framework, improves accuracy on 13 NLP benchmarks by compiling questions into Python programs and executing them, though gains are partly inflated by in-distribution retrieval.