Model confidence over time Several researchers have observed that model overconfidence (the model placing high probability on a small range of tokens) can cause poor quality generation (Jiang and de Rijke, 2018;Holtzman et al., 2019).
they show that for likelihood-maximizing decoding algorithms such as beam search, model confidence can increase in a snowball-like effect, getting stuck in a loop of repetitive but increasingly self-confident text.