@inproceedings{li-etal-2026-last,
title = "Last Layer Logits to Logic: Empowering {LLM}s with Logic-Consistent Structured Knowledge Reasoning",
author = "Li, Songze and
Liu, Zhiqiang and
Gong, Zhaoyan and
Bo, Zhongpu and
Gui, Zhengke and
Liang, Lei and
Chen, Huajun and
Zhang, Wen",
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.120/",
pages = "2780--2802",
ISBN = "979-8-89176-459-0",
abstract = "Large Language Models (LLMs) achieve excellent performance in natural language reasoning tasks through pre-training on vast unstructured text, enabling them to understand the logic in natural language and generate logic-consistent responses. However, the representational differences between unstructured and structured knowledge make LLMs inherently struggle to maintain logic consistency, leading to *Logic Drift* challenges in structured knowledge reasoning tasks such as Knowledge Graph Question Answering (KGQA). Existing methods address this limitation by designing complex workflows embedded in prompts to guide LLM reasoning. Nevertheless, these approaches only provide input-level guidance and fail to fundamentally address the *Logic Drift* in LLM outputs. Additionally, their inflexible reasoning workflows cannot adapt to different tasks and knowledge graphs. To enhance LLMs' logic consistency in structured knowledge reasoning, we specifically target the logits output from the autoregressive generation process. We propose the *Logits-to-Logic* framework, which incorporates logits strengthening and logits filtering as core modules to correct logical defects in LLM outputs. Extensive experiments show that our approach significantly improves LLMs' logic consistency in structured knowledge reasoning and achieves state-of-the-art performance on multiple KGQA benchmarks."
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<abstract>Large Language Models (LLMs) achieve excellent performance in natural language reasoning tasks through pre-training on vast unstructured text, enabling them to understand the logic in natural language and generate logic-consistent responses. However, the representational differences between unstructured and structured knowledge make LLMs inherently struggle to maintain logic consistency, leading to *Logic Drift* challenges in structured knowledge reasoning tasks such as Knowledge Graph Question Answering (KGQA). Existing methods address this limitation by designing complex workflows embedded in prompts to guide LLM reasoning. Nevertheless, these approaches only provide input-level guidance and fail to fundamentally address the *Logic Drift* in LLM outputs. Additionally, their inflexible reasoning workflows cannot adapt to different tasks and knowledge graphs. To enhance LLMs’ logic consistency in structured knowledge reasoning, we specifically target the logits output from the autoregressive generation process. We propose the *Logits-to-Logic* framework, which incorporates logits strengthening and logits filtering as core modules to correct logical defects in LLM outputs. Extensive experiments show that our approach significantly improves LLMs’ logic consistency in structured knowledge reasoning and achieves state-of-the-art performance on multiple KGQA benchmarks.</abstract>
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%0 Conference Proceedings
%T Last Layer Logits to Logic: Empowering LLMs with Logic-Consistent Structured Knowledge Reasoning
%A Li, Songze
%A Liu, Zhiqiang
%A Gong, Zhaoyan
%A Bo, Zhongpu
%A Gui, Zhengke
%A Liang, Lei
%A Chen, Huajun
%A Zhang, Wen
%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 li-etal-2026-last
%X Large Language Models (LLMs) achieve excellent performance in natural language reasoning tasks through pre-training on vast unstructured text, enabling them to understand the logic in natural language and generate logic-consistent responses. However, the representational differences between unstructured and structured knowledge make LLMs inherently struggle to maintain logic consistency, leading to *Logic Drift* challenges in structured knowledge reasoning tasks such as Knowledge Graph Question Answering (KGQA). Existing methods address this limitation by designing complex workflows embedded in prompts to guide LLM reasoning. Nevertheless, these approaches only provide input-level guidance and fail to fundamentally address the *Logic Drift* in LLM outputs. Additionally, their inflexible reasoning workflows cannot adapt to different tasks and knowledge graphs. To enhance LLMs’ logic consistency in structured knowledge reasoning, we specifically target the logits output from the autoregressive generation process. We propose the *Logits-to-Logic* framework, which incorporates logits strengthening and logits filtering as core modules to correct logical defects in LLM outputs. Extensive experiments show that our approach significantly improves LLMs’ logic consistency in structured knowledge reasoning and achieves state-of-the-art performance on multiple KGQA benchmarks.
%U https://aclanthology.org/2026.emnlp-main.120/
%P 2780-2802
Markdown (Informal)
[Last Layer Logits to Logic: Empowering LLMs with Logic-Consistent Structured Knowledge Reasoning](https://aclanthology.org/2026.emnlp-main.120/) (Li et al., EMNLP 2026)
ACL
- Songze Li, Zhiqiang Liu, Zhaoyan Gong, Zhongpu Bo, Zhengke Gui, Lei Liang, Huajun Chen, and Wen Zhang. 2026. Last Layer Logits to Logic: Empowering LLMs with Logic-Consistent Structured Knowledge Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 2780–2802, Budapest, Hungary. Association for Computational Linguistics.