arXiv:2606.19351v1 Announce Type: new Abstract: Knowledge graph (KG) reasoning infers new knowledge from existing facts and is widely applied in question answering, recommendation, and decision support. With the rapid development of large language models (LLMs), LLM-based KG reasoning frameworks have become increasingly popular by leveraging retrieved KG information. However, hallucinations in LLMs remain a critical issue. Even when relevant KG knowledge is incorporated, models may still generate incorrect outputs, leading to misinformation and unreliable decisions. Existing hallucination dete
The proliferation of large language models (LLMs) in knowledge-intensive applications demands robust methods to address their inherent hallucination problem, which currently limits their reliability.
Detecting and mitigating hallucinations is crucial for the widespread adoption and trustworthiness of LLM-based systems in critical applications like question answering and decision support, where misinformation can have significant consequences.
Improved hallucination detection moves LLM applications from experimental tools to more reliable and deployable solutions, enabling their use in sensitive domains.
- · AI developers
- · Enterprises adopting LLMs
- · Users of LLM-based systems
- · Providers of unreliable LLMs
- · Companies making decisions based on unverified LLM output
Increased trust and deployment of LLM-based knowledge graph reasoning systems across various industries.
Reduced incidence of misinformation and errors propagated by AI systems, leading to better decision-making.
Acceleration of AI integration into highly regulated sectors requiring high precision and verifiable outputs, potentially redefining professional workflows.
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