Last Layer Logits to Logic: Empowering LLMs with Logic-Consistent Structured Knowledge Reasoning
arXiv:2511.07910v2 Announce Type: replace 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 \textit{Logic Drift} challenges in structured knowledge reasoning tasks such as Knowledge Graph Question Answering (KGQA). Existing methods addres
This research addresses a critical limitation of Large Language Models (LLMs) which increasingly form the backbone of AI agents, highlighting the ongoing effort to enhance their logical reasoning capabilities.
Improving LLMs' ability to reason with structured knowledge consistently is crucial for their application in complex, high-stakes environments where logical accuracy is paramount.
The development of methods to mitigate 'Logic Drift' could lead to more reliable and trustworthy AI systems, particularly in domains requiring deep understanding of facts and relationships.
- · AI developers
- · Enterprise AI adoption
- · Knowledge graph technology
- · LLMs without logic-consistency improvements
- · Ad-hoc AI integration methods
LLMs can better integrate and reason with structured data sources like knowledge graphs.
Increased ability for AI agents to perform complex reasoning tasks autonomously, reducing human oversight requirements.
Acceleration of personalized, data-driven services and analytics, leading to new forms of automated decision-making across industries.
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Read at arXiv cs.CL