Hallucination occurs when a model disregards the source input, leading it to generate outputs based on prior knowledge rather than accurate translation. Exposure bias arises from discrepancies between training and inference data, resulting in unpredictable mistakes. Elena highlights the intricate relationship between these issues, suggesting that different training objectives may influence how often a model hallucinates.
