From Tokens to Semantics: Leveraging Complementary Signals for Hallucination Detection in Black-Box LLMs
cs.CL, cs.AI
Submitted: 2026-09-02
Updated: 2026-09-07
Terminology
Sources
- Improving Uncertainty Estimation through Semantically Diverse Language Generation
- Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
- Language Models (Mostly) Know What They Know
- Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation
- Semantic Energy: Detecting LLM Hallucination Beyond Entropy
- Uncertainty Estimation in Autoregressive Structured Prediction
- Estimating Semantic Alphabet Size for LLM Uncertainty Quantification
- Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity
- Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
- HALT: Hallucination Assessment via Log-probs as Time series
- HaluNet: Learning Hallucination Risk from Internal Signals in LLM Question Answering
- Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency
- Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach
- Uncertainty Under the Curve: A Sequence-Level Entropy Area Metric for Reasoning LLM
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