Summary of Postmark: a Robust Blackbox Watermark For Large Language Models, by Yapei Chang et al.
PostMark: A Robust Blackbox Watermark for Large Language Models
by Yapei Chang, Kalpesh Krishna, Amir Houmansadr, John Wieting, Mohit Iyyer
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to detect LLM-generated text by inserting a watermark during the decoding process. The authors develop PostMark, a post-hoc watermarking procedure that doesn’t require access to the underlying LLM’s logits. This is crucial since API providers are hesitant to share logit information due to fears of model distillation. PostMark inserts an input-dependent set of words into the text after decoding, making it a modular and implementable solution for third-party developers. The authors test their approach against eight baseline algorithms, five base LLMs, and three datasets, demonstrating its robustness to paraphrasing attacks. They also evaluate the impact on text quality using both automated and human assessments, highlighting the trade-off between quality and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PostMark is a new way to spot AI-generated text. The problem with current methods is that they need access to special information from the language model (LLM), which isn’t shared because it could be used to copy the model itself. PostMark doesn’t require this special information, making it something anyone can use. It works by adding specific words to the text after it’s been created, which makes it harder for people to fake the watermark. The authors tested their method against many different algorithms and datasets and found that it worked well. They also checked how good the resulting text was and how easy it was to spot as AI-generated. |
Keywords
» Artificial intelligence » Distillation » Language model » Logits
