@inproceedings{liang-etal-2026-diagnosing,
title = "Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in {KGQA}",
author = "Liang, Gewen and
Xu, Mufan and
Chen, Kehai and
Wang, Wei and
Wang, Yuwei and
Yang, Muyun and
Zhao, Tiejun and
Zhang, Min",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2086/",
pages = "45036--45054",
ISBN = "979-8-89176-390-6",
abstract = "Large language models (LLMs) have demonstrated increasingly strong reasoning capabilities, achieving remarkable progress in knowledge graph question answering (KGQA). However, a key challenge in such systems is non-deterministic reasoning, where the model indecisively activates multiple semantically related knowledge graph edges for a given query, frequently leading to incorrect answers. To address this issue, we propose Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA (DR2). DR2 identifies and localizes non-deterministic reasoning behaviors, uncovering the underlying semantic representation deficiencies in LLMs. Building on this diagnosis, we design abductive reasoning-based preference learning, which promotes fine-grained semantic discrimination and mitigates non-deterministic reasoning errors. Experimental results demonstrate that the proposed DR2 significantly outperforms several strong baselines, achieving state-of-the-art performance on the widely used WebQSP and CWQ benchmarks."
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<abstract>Large language models (LLMs) have demonstrated increasingly strong reasoning capabilities, achieving remarkable progress in knowledge graph question answering (KGQA). However, a key challenge in such systems is non-deterministic reasoning, where the model indecisively activates multiple semantically related knowledge graph edges for a given query, frequently leading to incorrect answers. To address this issue, we propose Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA (DR2). DR2 identifies and localizes non-deterministic reasoning behaviors, uncovering the underlying semantic representation deficiencies in LLMs. Building on this diagnosis, we design abductive reasoning-based preference learning, which promotes fine-grained semantic discrimination and mitigates non-deterministic reasoning errors. Experimental results demonstrate that the proposed DR2 significantly outperforms several strong baselines, achieving state-of-the-art performance on the widely used WebQSP and CWQ benchmarks.</abstract>
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%0 Conference Proceedings
%T Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA
%A Liang, Gewen
%A Xu, Mufan
%A Chen, Kehai
%A Wang, Wei
%A Wang, Yuwei
%A Yang, Muyun
%A Zhao, Tiejun
%A Zhang, Min
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F liang-etal-2026-diagnosing
%X Large language models (LLMs) have demonstrated increasingly strong reasoning capabilities, achieving remarkable progress in knowledge graph question answering (KGQA). However, a key challenge in such systems is non-deterministic reasoning, where the model indecisively activates multiple semantically related knowledge graph edges for a given query, frequently leading to incorrect answers. To address this issue, we propose Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA (DR2). DR2 identifies and localizes non-deterministic reasoning behaviors, uncovering the underlying semantic representation deficiencies in LLMs. Building on this diagnosis, we design abductive reasoning-based preference learning, which promotes fine-grained semantic discrimination and mitigates non-deterministic reasoning errors. Experimental results demonstrate that the proposed DR2 significantly outperforms several strong baselines, achieving state-of-the-art performance on the widely used WebQSP and CWQ benchmarks.
%U https://aclanthology.org/2026.acl-long.2086/
%P 45036-45054
Markdown (Informal)
[Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA](https://aclanthology.org/2026.acl-long.2086/) (Liang et al., ACL 2026)
ACL
- Gewen Liang, Mufan Xu, Kehai Chen, Wei Wang, Yuwei Wang, Muyun Yang, Tiejun Zhao, and Min Zhang. 2026. Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45036–45054, San Diego, California, United States. Association for Computational Linguistics.