@inproceedings{zhang-etal-2024-natural,
title = "Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning",
author = "Zhang, Tianhua and
Ge, Jiaxin and
Luo, Hongyin and
Chuang, Yung-Sung and
Gao, Mingye and
Gong, Yuan and
Kim, Yoon and
Wu, Xixin and
Meng, Helen and
Glass, James",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.259",
pages = "4131--4155",
abstract = "How can we perform computations over natural language representations to solve tasks that require symbolic and numeric reasoning? We propose natural language embedded programs (NLEP) as a unifying framework for addressing math/symbolic reasoning, natural language understanding, and instruction following tasks. Our approach prompts a language model to generate full Python programs that define functions over data structures which contain natural language representations of structured knowledge. A Python interpreter then executes the generated code and prints the output. Despite using a task-general prompt, we find that this approach can improve upon strong baselines across a range of different tasks including math and symbolic reasoning, text classification, question answering, and instruction following. We found that the generated programs are interpretable since they outline the exact reasoning process followed by the program interpreter.",
}
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<abstract>How can we perform computations over natural language representations to solve tasks that require symbolic and numeric reasoning? We propose natural language embedded programs (NLEP) as a unifying framework for addressing math/symbolic reasoning, natural language understanding, and instruction following tasks. Our approach prompts a language model to generate full Python programs that define functions over data structures which contain natural language representations of structured knowledge. A Python interpreter then executes the generated code and prints the output. Despite using a task-general prompt, we find that this approach can improve upon strong baselines across a range of different tasks including math and symbolic reasoning, text classification, question answering, and instruction following. We found that the generated programs are interpretable since they outline the exact reasoning process followed by the program interpreter.</abstract>
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%0 Conference Proceedings
%T Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning
%A Zhang, Tianhua
%A Ge, Jiaxin
%A Luo, Hongyin
%A Chuang, Yung-Sung
%A Gao, Mingye
%A Gong, Yuan
%A Kim, Yoon
%A Wu, Xixin
%A Meng, Helen
%A Glass, James
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zhang-etal-2024-natural
%X How can we perform computations over natural language representations to solve tasks that require symbolic and numeric reasoning? We propose natural language embedded programs (NLEP) as a unifying framework for addressing math/symbolic reasoning, natural language understanding, and instruction following tasks. Our approach prompts a language model to generate full Python programs that define functions over data structures which contain natural language representations of structured knowledge. A Python interpreter then executes the generated code and prints the output. Despite using a task-general prompt, we find that this approach can improve upon strong baselines across a range of different tasks including math and symbolic reasoning, text classification, question answering, and instruction following. We found that the generated programs are interpretable since they outline the exact reasoning process followed by the program interpreter.
%U https://aclanthology.org/2024.findings-naacl.259
%P 4131-4155
Markdown (Informal)
[Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning](https://aclanthology.org/2024.findings-naacl.259) (Zhang et al., Findings 2024)
ACL
- Tianhua Zhang, Jiaxin Ge, Hongyin Luo, Yung-Sung Chuang, Mingye Gao, Yuan Gong, Yoon Kim, Xixin Wu, Helen Meng, and James Glass. 2024. Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 4131–4155, Mexico City, Mexico. Association for Computational Linguistics.