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From Tokens to Semantics: Leveraging Complementary Signals for Hallucination Detection in Black-Box LLMs

Paper recorded by Signals 4 on 2026-09-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-02 on arXiv · recorded by Signals 4 on 2026-09-03

Category: cs.AI · 人工智能 · first seen 2026-09-03

Abstract

When LLMs support public-facing or high-stakes workflows, missed fabrications can harm users and institutions, while false alarms consume limited human-review capacity. When no trusted context or reference document is available, we study two signals accessible through black-box model APIs: semantic entropy, which measures disagreement among sampled response meanings, and uncertainty derived from t

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#219 most recent of 300 cs.AI papers we have recorded · ↑ newer: DKL: Decoupled Knowledge Learning for Instruction-Tuned Language Model · ↓ older: Loom: Weaving Diagnostic Strands into Free-Text Consensus via Embeddin
Cite this page: From Tokens to Semantics: Leveraging Complementary Signals for Hallucination Detection in Black-Box LLMs: the #219 most recent of 300 cs.AI papers we have recorded (as of 2026-09-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/from-tokens-to-semantics-leveraging-complementary-signals-for-hallucination-dete.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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