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        <title>Abstract-level Deductive Reasoning for Pre-trained Language Models</title>
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    <abstract>Pre-trained Language Models have been shown to be able to emulate deductive reasoning in natural language. However, PLMs are easily affected by irrelevant information (e.g., entity) in instance-level proofs when learning deductive reasoning. To address this limitation, we propose an Abstract-level Deductive Reasoner (ADR). ADR is trained to predict the abstract reasoning proof of each sample, which guides PLMs to learn general reasoning patterns rather than instance-level knowledge. Experimental results demonstrate that ADR significantly reduces the impact of PLMs learning instance-level knowledge (over 70%).</abstract>
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%0 Conference Proceedings
%T Abstract-level Deductive Reasoning for Pre-trained Language Models
%A Wu, Xin
%A Cai, Yi
%A Leung, Ho-fung
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F wu-etal-2024-abstract
%X Pre-trained Language Models have been shown to be able to emulate deductive reasoning in natural language. However, PLMs are easily affected by irrelevant information (e.g., entity) in instance-level proofs when learning deductive reasoning. To address this limitation, we propose an Abstract-level Deductive Reasoner (ADR). ADR is trained to predict the abstract reasoning proof of each sample, which guides PLMs to learn general reasoning patterns rather than instance-level knowledge. Experimental results demonstrate that ADR significantly reduces the impact of PLMs learning instance-level knowledge (over 70%).
%U https://aclanthology.org/2024.lrec-main.6/
%P 70-76

Markdown (Informal)

[Abstract-level Deductive Reasoning for Pre-trained Language Models](https://aclanthology.org/2024.lrec-main.6/) (Wu et al., LREC-COLING 2024)

• Abstract-level Deductive Reasoning for Pre-trained Language Models (Wu et al., LREC-COLING 2024)

ACL

• Xin Wu, Yi Cai, and Ho-fung Leung. 2024. Abstract-level Deductive Reasoning for Pre-trained Language Models. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 70–76, Torino, Italia. ELRA and ICCL.