Abstract:Detecting machine-generated text is important for transparency and accountability when deploying large language models (LLMs). Among detection approaches, watermarking is a statistically reliable method 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 high-quality-tunes an LLM by way of reinforcement learning to generate text that's each 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 task-particular metric. Experiments on lengthy-form QA, summarization, and essay writing present that LLMs trained with PUPPET achieve high detectability aggressive with watermarking methods while outperforming them on downstream duties. The evaluation shows that this optimization will be carried out efficiently with only a few thousand samples in 1--2 GPU hours. Moreover, these positive aspects are constant throughout out-of-domain duties, completely different LLM families, and mannequin sizes, and are even robust to paraphrasing assaults.

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