Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation

Understand

Current language generation models suffer from issues such as repetition, incoherence, and hallucinations.

• An often-repeated hypothesis is that this brittleness of generation models is caused by the training and the generation procedure mismatch, also referred to as exposure bias.
• In this paper, we verify this hypothesis by analyzing exposure bias from an imitation learning perspective.
• We show that exposure bias leads to an accumulation of errors, analyze why perplexity fails to capture this accumulation, and empirically show that this accumulation results in poor generation quality.