Performance Trade-offs of Watermarking Large Language Models
Anirudh Ajith Sameer Singh Danish Pruthi
Amidst growing concerns of large language models (LLMs) being misused for
generating misinformation or completing homework assignments, watermarking has
emerged as an effective solution for distinguishing human-written and
LLM-generated text. A prominent watermarking strategy is to embed a signal into
generated text by upsampling a (pseudorandomly-chosen) subset of tokens at
every generation step. Although this signal is imperceptible to a human reader,
it is detectable through statistical testing. However, implanting such signals
alters the model's output distribution and can have unintended effects when
watermarked LLMs are used for downstream applications. In this work, we
evaluate the performance of watermarked LLMs on a diverse suite of tasks,
including text classification, textual entailment, reasoning, question
answering, translation, summarization, and language modeling. We find that
watermarking has negligible impact on the performance of tasks posed as k-class
classification problems in the average case. However, the accuracy can plummet
to that of a random classifier for some scenarios (that occur with
non-negligible probability). Tasks that are cast as multiple-choice questions
and short-form generation are surprisingly unaffected by watermarking. For
long-form generation tasks, including summarization and translation, we see a
drop of 15-20% in the performance due to watermarking. Our findings highlight
the trade-offs that users should be cognizant of when using watermarked models,
and point to cases where future research could improve existing trade-offs.