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BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models

arXiv 2606.07528 ↗First posted 20 Apr 2026cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

Hallucination in large language models (LLMs), defined as the generation of factually incorrect or unsupported content, remains a critical barrier to reliable deployment. We present BEACON (Behavioral Entropy Aggregation for Cross-model hallucination detectiON), a black-box hallucination detection framework that operates purely on model outputs without requiring access to internal representations or external knowledge bases. BEACON extracts a 31-dimensional feature vector from structured multi-pass generation, integrating NLI-based semantic entropy, embedding geometry, chain-of-thought consistency, and paraphrase stability signals. A gradient-boosted classifier trained on 7,617 labeled examples across seven benchmarks achieves 0.8123 +/- 0.0102 AUROC (95% CI: 0.7632-0.8251), outperforming standalone semantic entropy (+0.2298) and SelfCheckGPT-style consistency baselines (+0.2457). Feature importance analysis shows that hallucination is inherently multi-dimensional, requiring combined uncertainty signals. An efficient 5-call variant achieves 0.7795 AUROC, enabling practical deployment across black-box LLM APIs.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 12 references that are in the map. First posted on arXiv 20 Apr 2026.

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References · 11

  1. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  2. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  3. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,414
  4. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  5. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  6. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 893
  7. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 795
  8. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 788
  9. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  10. 2021Tabular Data: Deep Learning is Not All You Needcited by 530
  11. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329

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  1. 2025D2HScore: Reasoning-Aware Hallucination Detection via Semantic Breadth and Depth Analysis in LLMs
  2. 2026AURORA: Asymmetry and Update-Induced Rotation for Robust Hallucination Detection in Large Language Models
  3. 2026Domain-Specific Hallucination Detection in Large Language Models
  4. 2026Scalable Token-Level Hallucination Detection in Large Language Models
  5. 2025SelfCheck-Eval: A Multi-Module Framework for Zero-Resource Hallucination Detection in Large Language Models
  6. 2026Predictive Coding and Information Bottleneck for Hallucination Detection in Large Language Models
  7. 2025Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection
  8. 2025Revisiting Hallucination Detection with Effective Rank-based Uncertainty