Abstract:Detecting machine-generated text is crucial for transparency and accountability when deploying massive language
fashions (LLMs). Among detection approaches, watermarking is a
statistically reliable technique by design -- it embeds detectable
indicators into LLM outputs by biasing their token distributions.
However, it has been reported that watermarked LLMs typically carry out worse
on downstream duties. We suggest PUPPET, a framework that advantageous-tunes an LLM through reinforcement studying to generate textual content that's both more detectable and higher performing
on downstream duties. We use two reward functions:
a detector that outputs a machine-class chance and an evaluator that measures a activity-particular metric.
Experiments on lengthy-type QA, summarization, and essay writing present that LLMs skilled with PUPPET achieve excessive detectability competitive with watermarking
strategies whereas outperforming them on downstream duties.
The analysis exhibits that this optimization could be performed effectively with only
a few thousand samples in 1--2 GPU hours. Moreover, these positive aspects
are constant across out-of-domain duties, totally different LLM households, and mannequin sizes, and are even sturdy to paraphrasing assaults.
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