SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation
Keywords: AI-generated text detection, large language model, natural language watermark, locality-sensitive hashing, paraphrase attack, sentence encoder, contrastive learning
TL;DR: Propose a semantic watermark for large language model generations and discover
Abstract: Existing watermarking algorithms are vulnerable to paraphrase attacks because of their token-level design.
To address this issue, we propose SemStamp, a robust sentence-level semantic watermarking algorithm based on locality-sensitive hashing (LSH), which partitions the semantic space of sentences. The algorithm encodes and LSH-hashes a candidate sentence generated by an LLM, and conducts sentence-level rejection sampling until the sampled sentence falls in watermarked partitions in the semantic embedding space. A margin-based constraint is used to enhance its robustness. To show the advantages of our algorithm, we propose a "bigram" paraphrase attack using the paraphrase that has the fewest bigram overlaps with the original sentence. This attack is shown to be effective against the existing token-level watermarking method. Experimental results show that our novel semantic watermark algorithm is not only more robust than the previous state-of-the-art method on both common and bigram paraphrase attacks, but also is better at preserving the quality of generation.
Primary Area: generative models
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Submission Number: 6692
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