arXiv:2605.12456v2 Announce Type: replace-cross Abstract: We introduce TextSeal, a state-of-the-art watermark for large language models. Building on Gumbel-max sampling, TextSeal introduces dual-key generation to restore output diversity, along with entropy-weighted scoring and multi-region localization for improved detection. It supports serving optimizations such as speculative decoding and multi-token prediction, and does not add any inference overhead. TextSeal strictly dominates baselines like SynthID-text in detection strength and is robust to dilution, maintaining confident localized de
The proliferation of LLMs and concerns around provenance, misinformation, and intellectual property theft necessitate advanced watermarking solutions to maintain trust and accountability.
Sophisticated watermarking like TextSeal is critical for proving the origin of LLM-generated content, protecting proprietary models, and enabling responsible AI deployment in sensitive applications.
The ability to confidently identify content generated by specific LLMs, even after modifications, enhances trust and accountability while potentially altering business models for content creation and AI services.
- · LLM developers
- · Content creators
- · IP holders
- · AI ethics and governance bodies
- · Misinformation actors
- · Plagiarists
- · Unauthorized LLM distillers
More secure and traceable LLM outputs will become standard for enterprise and critical applications.
The development of robust watermarking capabilities could lead to new regulatory frameworks for AI-generated content and liability.
Increased trust in AI provenance might accelerate the adoption of LLMs in highly sensitive sectors, potentially replacing human-generated content where traceability is paramount.
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Read at arXiv cs.LG