Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity
arXiv:2606.10198v1 Announce Type: new Abstract: Hallucination detection in large language and vision-language models is increasingly framed as selective prediction, where a detector assigns a confidence score and abstains when confidence is low. Unsupervised sampling detectors (Semantic Entropy, EigenScore) avoid labels but plateau in quality, while supervised probes (SAPLMA) attain stronger in-distribution scores yet degrade sharply when calibration labels are scarce. We recover the response manifold of an LLM as the density ridge of a kernel density estimate built on a six-dimensional kinema
The proliferation of LLMs and VLMs across various applications necessitates robust methods for detecting and mitigating hallucinations to ensure reliability and trustworthiness.
Improving the accuracy and reliability of AI models directly impacts their adoption in critical applications and the efficiency of AI-powered workflows.
New techniques for hallucination detection, particularly those effective under scarce calibration data, will enhance the trustworthiness and deployment of advanced AI systems.
- · AI developers
- · Enterprises deploying LLMs/VLMs
- · AI-Agent companies
- · AI research institutions
- · Companies relying on unreliable 'black box' AI
- · Applications with high-risk hallucination potential
More reliable AI models lead to increased confidence in AI-driven decision-making.
The reduced risk of hallucinations could accelerate the deployment of autonomous AI agents in sensitive domains.
This could lead to a broader societal acceptance and integration of advanced AI, potentially transforming numerous industries and professional roles.
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Read at arXiv cs.LG