Papers with detection tool

2 papers
LLMDet: A Third Party Large Language Models Generated Text Detection Tool (2023.findings-emnlp)

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Challenge: Existing detection tools rely on access to LLMs and can only distinguish between machine-generated and human-authored text.
Approach: They propose a model-specific, secure, efficient, and extendable detection tool that can source text from specific LLMs.
Outcome: The proposed tool can source text from specific LLMs, such as GPT-2, OPT, LLaMA, and others.
Position: LLM Watermarking Should Align Stakeholders’ Incentives for Practical Adoption (2026.findings-acl)

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Challenge: despite advances in watermarking algorithms, real-world deployment remains limited . model watermarks can be used to protect intellectual property and promote trust in AI .
Approach: They propose to revisit three classes of watermarking to examine incentives for large language models . model watermarks naturally aligns with LLM provider interests, yet faces new challenges in open-source ecosystems .
Outcome: The proposed methods can be used in dataset decontamination, user-controlled provenance, and in-context watermarking.

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