@inproceedings{hasegawa-etal-2026-neuro,
title = "Neuro-Symbolic Reasoning with External Inference Tools Mitigates Bias from Sub-Formula Level Logical Structure",
author = "Hasegawa, Ryo and
Sakai, Yusuke and
Kamigaito, Hidetaka and
Watanabe, Taro",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2564/",
pages = "58262--58284",
ISBN = "979-8-89176-459-0",
abstract = "Logical reasoning is a core ability of modern language models, and various approaches have been proposed to enhance it. One effective method is neuro-symbolic approach, which makes a model output logic formulas and predict the answer based on them. However, fair comparison is challenging because existing methods use different experimental settings, such as post-processing and prompt styles. In addition, existing error analyses are too coarse to capture the impact of sub-formula level structures. In this paper, we present a systematic analysis of neuro-symbolic approaches under a unified setting with sub-formula level annotation. We reveal that some sub-formula level structures bias the model{'}s inference toward assuming the answer is true, and neuro-symbolic approaches with an external inference tool help mitigate this bias. On the other hand, these methods sometimes lack flexibility in representing sentence meaning as logic formulas."
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<abstract>Logical reasoning is a core ability of modern language models, and various approaches have been proposed to enhance it. One effective method is neuro-symbolic approach, which makes a model output logic formulas and predict the answer based on them. However, fair comparison is challenging because existing methods use different experimental settings, such as post-processing and prompt styles. In addition, existing error analyses are too coarse to capture the impact of sub-formula level structures. In this paper, we present a systematic analysis of neuro-symbolic approaches under a unified setting with sub-formula level annotation. We reveal that some sub-formula level structures bias the model’s inference toward assuming the answer is true, and neuro-symbolic approaches with an external inference tool help mitigate this bias. On the other hand, these methods sometimes lack flexibility in representing sentence meaning as logic formulas.</abstract>
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%0 Conference Proceedings
%T Neuro-Symbolic Reasoning with External Inference Tools Mitigates Bias from Sub-Formula Level Logical Structure
%A Hasegawa, Ryo
%A Sakai, Yusuke
%A Kamigaito, Hidetaka
%A Watanabe, Taro
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hasegawa-etal-2026-neuro
%X Logical reasoning is a core ability of modern language models, and various approaches have been proposed to enhance it. One effective method is neuro-symbolic approach, which makes a model output logic formulas and predict the answer based on them. However, fair comparison is challenging because existing methods use different experimental settings, such as post-processing and prompt styles. In addition, existing error analyses are too coarse to capture the impact of sub-formula level structures. In this paper, we present a systematic analysis of neuro-symbolic approaches under a unified setting with sub-formula level annotation. We reveal that some sub-formula level structures bias the model’s inference toward assuming the answer is true, and neuro-symbolic approaches with an external inference tool help mitigate this bias. On the other hand, these methods sometimes lack flexibility in representing sentence meaning as logic formulas.
%U https://aclanthology.org/2026.emnlp-main.2564/
%P 58262-58284
Markdown (Informal)
[Neuro-Symbolic Reasoning with External Inference Tools Mitigates Bias from Sub-Formula Level Logical Structure](https://aclanthology.org/2026.emnlp-main.2564/) (Hasegawa et al., EMNLP 2026)
ACL