Hallucination in LLMs Survey
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A comprehensive survey of hallucination in LLMs, covering taxonomy, causes, evaluation, and mitigation.
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.
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