Reverse-Engineering Decoding Strategies Given Blackbox Access to a
  Language Generation System
Daphne Ippolito Nicholas Carlini Katherine Lee Milad Nasr Yun William Yu
Neural language models are increasingly deployed into APIs and websites that
allow a user to pass in a prompt and receive generated text. Many of these
systems do not reveal generation parameters. In this paper, we present methods
to reverse-engineer the decoding method used to generate text (i.e., top-$k$ or
nucleus sampling). Our ability to discover which decoding strategy was used has
implications for detecting generated text. Additionally, the process of
discovering the decoding strategy can reveal biases caused by selecting
decoding settings which severely truncate a model's predicted distributions. We
perform our attack on several families of open-source language models, as well
as on production systems (e.g., ChatGPT).