@inproceedings{pi-etal-2022-reasoning,
title = "Reasoning Like Program Executors",
author = "Pi, Xinyu and
Liu, Qian and
Chen, Bei and
Ziyadi, Morteza and
Lin, Zeqi and
Fu, Qiang and
Gao, Yan and
Lou, Jian-Guang and
Chen, Weizhu",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.48",
doi = "10.18653/v1/2022.emnlp-main.48",
pages = "761--779",
abstract = "Reasoning over natural language is a long-standing goal for the research community. However, studies have shown that existing language models are inadequate in reasoning. To address the issue, we present POET, a novel reasoning pre-training paradigm. Through pre-training language models with programs and their execution results, POET empowers language models to harvest the reasoning knowledge possessed by program executors via a data-driven approach. POET is conceptually simple and can be instantiated by different kinds of program executors. In this paper, we showcase two simple instances POET-Math and POET-Logic, in addition to a complex instance, POET-SQL. Experimental results on six benchmarks demonstrate that POET can significantly boost model performance in natural language reasoning, such as numerical reasoning, logical reasoning, and multi-hop reasoning. POET opens a new gate on reasoning-enhancement pre-training, and we hope our analysis would shed light on the future research of reasoning like program executors.",
}
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<abstract>Reasoning over natural language is a long-standing goal for the research community. However, studies have shown that existing language models are inadequate in reasoning. To address the issue, we present POET, a novel reasoning pre-training paradigm. Through pre-training language models with programs and their execution results, POET empowers language models to harvest the reasoning knowledge possessed by program executors via a data-driven approach. POET is conceptually simple and can be instantiated by different kinds of program executors. In this paper, we showcase two simple instances POET-Math and POET-Logic, in addition to a complex instance, POET-SQL. Experimental results on six benchmarks demonstrate that POET can significantly boost model performance in natural language reasoning, such as numerical reasoning, logical reasoning, and multi-hop reasoning. POET opens a new gate on reasoning-enhancement pre-training, and we hope our analysis would shed light on the future research of reasoning like program executors.</abstract>
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%0 Conference Proceedings
%T Reasoning Like Program Executors
%A Pi, Xinyu
%A Liu, Qian
%A Chen, Bei
%A Ziyadi, Morteza
%A Lin, Zeqi
%A Fu, Qiang
%A Gao, Yan
%A Lou, Jian-Guang
%A Chen, Weizhu
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F pi-etal-2022-reasoning
%X Reasoning over natural language is a long-standing goal for the research community. However, studies have shown that existing language models are inadequate in reasoning. To address the issue, we present POET, a novel reasoning pre-training paradigm. Through pre-training language models with programs and their execution results, POET empowers language models to harvest the reasoning knowledge possessed by program executors via a data-driven approach. POET is conceptually simple and can be instantiated by different kinds of program executors. In this paper, we showcase two simple instances POET-Math and POET-Logic, in addition to a complex instance, POET-SQL. Experimental results on six benchmarks demonstrate that POET can significantly boost model performance in natural language reasoning, such as numerical reasoning, logical reasoning, and multi-hop reasoning. POET opens a new gate on reasoning-enhancement pre-training, and we hope our analysis would shed light on the future research of reasoning like program executors.
%R 10.18653/v1/2022.emnlp-main.48
%U https://aclanthology.org/2022.emnlp-main.48
%U https://doi.org/10.18653/v1/2022.emnlp-main.48
%P 761-779
Markdown (Informal)
[Reasoning Like Program Executors](https://aclanthology.org/2022.emnlp-main.48) (Pi et al., EMNLP 2022)
ACL
- Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Qiang Fu, Yan Gao, Jian-Guang Lou, and Weizhu Chen. 2022. Reasoning Like Program Executors. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 761–779, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.