Codifying Natural Langauge Tasks

Fuente: arXiv
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Autores principales: Chen, Haoyang, Tanaka-Ishii, Kumiko
Formato: Preprint
Publicado: 2025
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author Chen, Haoyang
Tanaka-Ishii, Kumiko
author_facet Chen, Haoyang
Tanaka-Ishii, Kumiko
contents We explore the applicability of text-to-code to solve real-world problems that are typically solved in natural language, such as legal judgment and medical QA. Unlike previous works, our approach leverages the explicit reasoning provided by program generation. We present ICRAG, a framework that transforms natural language into executable programs through iterative refinement using external knowledge from domain resources and GitHub. Across 13 benchmarks, ICRAG achieves up to 161.1\% relative improvement. We provide a detailed analysis of the generated code and the impact of external knowledge, and we discuss the limitations of applying text-to-code approaches to real-world natural language tasks.
format Preprint
id arxiv_https___arxiv_org_abs_2509_17455
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Codifying Natural Langauge Tasks
Chen, Haoyang
Tanaka-Ishii, Kumiko
Computation and Language
Artificial Intelligence
We explore the applicability of text-to-code to solve real-world problems that are typically solved in natural language, such as legal judgment and medical QA. Unlike previous works, our approach leverages the explicit reasoning provided by program generation. We present ICRAG, a framework that transforms natural language into executable programs through iterative refinement using external knowledge from domain resources and GitHub. Across 13 benchmarks, ICRAG achieves up to 161.1\% relative improvement. We provide a detailed analysis of the generated code and the impact of external knowledge, and we discuss the limitations of applying text-to-code approaches to real-world natural language tasks.
title Codifying Natural Langauge Tasks
topic Computation and Language
Artificial Intelligence
url https://arxiv.org/abs/2509.17455