@inproceedings{aoki-etal-2023-empirical,
title = "Empirical Investigation of Neural Symbolic Reasoning Strategies",
author = "Aoki, Yoichi and
Kudo, Keito and
Kuribayashi, Tatsuki and
Brassard, Ana and
Yoshikawa, Masashi and
Sakaguchi, Keisuke and
Inui, Kentaro",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.86",
doi = "10.18653/v1/2023.findings-eacl.86",
pages = "1154--1162",
abstract = "Neural reasoning accuracy improves when generating intermediate reasoning steps. However, the source of this improvement is yet unclear. Here, we investigate and factorize the benefit of generating intermediate steps for symbolic reasoning. Specifically, we decompose the reasoning strategy w.r.t. step granularity and chaining strategy. With a purely symbolic numerical reasoning dataset (e.g., A=1, B=3, C=A+3, C?), we found that the choice of reasoning strategies significantly affects the performance, with the gap becoming even larger as the extrapolation length becomes longer. Surprisingly, we also found that certain configurations lead to nearly perfect performance, even in the case of length extrapolation. Our results indicate the importance of further exploring effective strategies for neural reasoning models.",
}
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<abstract>Neural reasoning accuracy improves when generating intermediate reasoning steps. However, the source of this improvement is yet unclear. Here, we investigate and factorize the benefit of generating intermediate steps for symbolic reasoning. Specifically, we decompose the reasoning strategy w.r.t. step granularity and chaining strategy. With a purely symbolic numerical reasoning dataset (e.g., A=1, B=3, C=A+3, C?), we found that the choice of reasoning strategies significantly affects the performance, with the gap becoming even larger as the extrapolation length becomes longer. Surprisingly, we also found that certain configurations lead to nearly perfect performance, even in the case of length extrapolation. Our results indicate the importance of further exploring effective strategies for neural reasoning models.</abstract>
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%0 Conference Proceedings
%T Empirical Investigation of Neural Symbolic Reasoning Strategies
%A Aoki, Yoichi
%A Kudo, Keito
%A Kuribayashi, Tatsuki
%A Brassard, Ana
%A Yoshikawa, Masashi
%A Sakaguchi, Keisuke
%A Inui, Kentaro
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F aoki-etal-2023-empirical
%X Neural reasoning accuracy improves when generating intermediate reasoning steps. However, the source of this improvement is yet unclear. Here, we investigate and factorize the benefit of generating intermediate steps for symbolic reasoning. Specifically, we decompose the reasoning strategy w.r.t. step granularity and chaining strategy. With a purely symbolic numerical reasoning dataset (e.g., A=1, B=3, C=A+3, C?), we found that the choice of reasoning strategies significantly affects the performance, with the gap becoming even larger as the extrapolation length becomes longer. Surprisingly, we also found that certain configurations lead to nearly perfect performance, even in the case of length extrapolation. Our results indicate the importance of further exploring effective strategies for neural reasoning models.
%R 10.18653/v1/2023.findings-eacl.86
%U https://aclanthology.org/2023.findings-eacl.86
%U https://doi.org/10.18653/v1/2023.findings-eacl.86
%P 1154-1162
Markdown (Informal)
[Empirical Investigation of Neural Symbolic Reasoning Strategies](https://aclanthology.org/2023.findings-eacl.86) (Aoki et al., Findings 2023)
ACL
- Yoichi Aoki, Keito Kudo, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi, and Kentaro Inui. 2023. Empirical Investigation of Neural Symbolic Reasoning Strategies. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1154–1162, Dubrovnik, Croatia. Association for Computational Linguistics.