A DETR-style probe distills multi-sample claim uncertainty into single-pass span detection and continuous Mixture-of-Beta scores, outperforming baselines on a new 293K-span benchmark.
Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models,
2 Pith papers cite this work. Polarity classification is still indexing.
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SemHash-LLM is a multi-granularity semantic hashing framework that fuses character, token, and document signals via gated fusion and cascaded filtering to achieve high-quality deduplication with under 1% neural verification cost.
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SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation
A DETR-style probe distills multi-sample claim uncertainty into single-pass span detection and continuous Mixture-of-Beta scores, outperforming baselines on a new 293K-span benchmark.
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SemHash-LLM: A Multi-Granularity Semantic Hashing Framework for Document Deduplication
SemHash-LLM is a multi-granularity semantic hashing framework that fuses character, token, and document signals via gated fusion and cascaded filtering to achieve high-quality deduplication with under 1% neural verification cost.