To Burst or Not to Burst: Generating and Quantifying Improbable Text
Kuleen Sasse Samuel Barham Efsun Sarioglu Kayi Edward W. Staley
While large language models (LLMs) are extremely capable at text generation,
their outputs are still distinguishable from human-authored text. We explore
this separation across many metrics over text, many sampling techniques, many
types of text data, and across two popular LLMs, LLaMA and Vicuna. Along the
way, we introduce a new metric, recoverability, to highlight differences
between human and machine text; and we propose a new sampling technique, burst
sampling, designed to close this gap. We find that LLaMA and Vicuna have
distinct distributions under many of the metrics, and that this influences our
results: Recoverability separates real from fake text better than any other
metric when using LLaMA. When using Vicuna, burst sampling produces text which
is distributionally closer to real text compared to other sampling techniques.