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papersSEP 12 04:00 UTC

Paper Proposes Multi-Signal Pipeline for Detecting LLM Hallucinations

A new arXiv preprint describes a hallucination detection method that combines a fine-tuned DeBERTa-v3 classifier with Monte Carlo dropout uncertainty estimates and other signals. The approach targets domain-specific settings where unfaithful model outputs are a concern. The work is cross-listed under cs.AI.

arXivDeBERTa-v3LLM hallucination detectionMonte Carlo dropoutUncertainty estimationcs.AI

COVERAGE · 3 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGDomain-Specific Hallucination Detection in Large Language Models ↗SEP 11 04:00 UTC
arXiv cs.CLDomain-Specific Hallucination Detection in Large Language Models ↗SEP 11 04:00 UTC
arXiv cs.AIDomain-Specific Hallucination Detection in Large Language Models ↗SEP 12 04:00 UTC

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