Hallucination in LLMs Survey

Two-category taxonomy: Separates hallucinations into factuality hallucinations (incorrect facts) and faithfulness hallucinations (deviations from source content).

Causes breakdown: Attributes hallucinations to training-data issues, training-stage artifacts, and inference-time choices - each with distinct mitigation paths.

Evaluation landscape: Reviews benchmarks and automatic metrics specifically designed for hallucination, contrasting them with general-purpose LLM metrics.

Mitigation strategies: Organizes mitigation into data curation, training-stage (RLHF, factuality tuning), and inference-stage (decoding, retrieval) approaches.