Hallucination and "parroting"

Detecting, Evaluating, and Reducing Hallucinations

Detecting hallucinations involves distinguishing accurate outputs from those that deviate from factual or contextual grounding. One approach is consistency checking, where LLM outputs are evaluated against external knowledge bases to identify discrepancies. Manakul et al. (2023) propose SelfCheckGPT, a zero-resource method that uses the model’s internal consistency to detect

Conceptual Contrasts Between Parroting and Hallucination in Language Models

Advancements in artificial intelligence (AI), particularly in natural language processing (NLP), highlight critical distinctions between parroting and hallucination in language models. Parroting refers to AI reproducing or mimicking patterns and phrases from training data without demonstrating understanding or creativity. Hallucination involves generating factually incorrect, implausible, or fabricated outputs, often diverging