🛡️MediumAlignment & SafetyPREMIUM

Hallucination Detection & Mitigation

Master the taxonomy, detection methods, and mitigation strategies for LLM hallucinations. Covers everything from SelfCheckGPT and semantic entropy to specialized detectors like Lynx, token-level probing, and cutting-edge prevention techniques including contrastive decoding.

What you'll master

Hallucination taxonomy (intrinsic vs. extrinsic, factual vs. faithfulness)

SelfCheckGPT and zero-resource detection

Lynx and specialized hallucination detection models

Semantic entropy for uncertainty quantification

Token-level probing of hidden states

Context compression and 'lost in the middle' phenomenon

Retrieval-augmented grounding

Chain-of-Verification (CoVe)

Confidence calibration and selective abstention

Citation verification and attribution

Multi-Agent Systems (MAS) for verification

Contrastive decoding for hallucination prevention

DoLa (Decoding by Contrasting Layers)

Medium55 min readIncludes code examples, architecture diagrams, and expert-level follow-up questions.

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