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Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedy. (arXiv:2307.13808v1 [cs.CL])
cs.CR updates on arXiv.org arxiv.org
To mitigate potential risks associated with language models, recent AI
detection research proposes incorporating watermarks into machine-generated
text through random vocabulary restrictions and utilizing this information for
detection. While these watermarks only induce a slight deterioration in
perplexity, our empirical investigation reveals a significant detriment to the
performance of conditional text generation. To address this issue, we introduce
a simple yet effective semantic-aware watermarking algorithm that considers the
characteristics of conditional text generation and the input context.
Experimental results demonstrate …
aware challenges detection generated information investigation language language models machine random remedy research restrictions risks text vocabulary watermarking watermarks