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Abstract:Detecting machine-generated textual content is essential for transparency and accountability when deploying large language fashions （LLMs）.

Among detection approaches watermarking is a statistically reliable methodology by design -- it embeds detectable alerts into LLM outputs by biasing their token distributions. However it has been reported that watermarked LLMs usually perform worse on downstream tasks.
We suggest PUPPET a framework that advantageous-tunes an LLM via reinforcement learning to generate text that‘s both extra detectable and better performing on downstream duties. We use two reward capabilities: a detector that outputs a machine-class probability and an evaluator that measures a job-specific metric.
Experiments on long-form QA summarization and essay writing present that LLMs skilled with PUPPET achieve high detectability aggressive with watermarking strategies whereas outperforming them on downstream duties. The analysis exhibits that this optimization might be performed ef

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