  Large language models (LLMs) often exhibit undesirable behaviors, such as
hallucinations and sequence repetitions. We propose to view these behaviors as
fallbacks that models exhibit under uncertainty, and investigate the connection
between them. We categorize fallback behaviors -- sequence repetitions,
degenerate text, and hallucinations -- and extensively analyze them in models
from the same family that differ by the amount of pretraining tokens, parameter
count, or the inclusion of instruction-following training. Our experiments
reveal a clear and consistent ordering of fallback behaviors, across all these
axes: the more advanced an LLM is (i.e., trained on more tokens, has more
parameters, or instruction-tuned), its fallback behavior shifts from sequence
repetitions, to degenerate text, and then to hallucinations. Moreover, the same
ordering is observed throughout a single generation, even for the
best-performing models; as uncertainty increases, models shift from generating
hallucinations to producing degenerate text and then sequence repetitions.
Lastly, we demonstrate that while common decoding techniques, such as random
sampling, might alleviate some unwanted behaviors like sequence repetitions,
they increase harder-to-detect hallucinations.
