Summary of Credid: Credible Multi-bit Watermark For Large Language Models Identification, by Haoyu Jiang et al.
CredID: Credible Multi-Bit Watermark for Large Language Models Identification
by Haoyu Jiang, Xuhong Wang, Ping Yi, Shanzhe Lei, Yilun Lin
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed multi-party credible watermarking framework (CredID) addresses privacy concerns in Large Language Models (LLMs) by involving a trusted third party (TTP) and multiple LLM vendors. The framework consists of watermark embedding and extraction stages, where vendors request seeds from the TTP to generate watermarked text without sharing user prompts. The TTP coordinates each vendor to extract and verify the watermark, providing a credible watermarking scheme while preserving vendor privacy. Additionally, the paper proposes a novel multi-bit watermarking algorithm and an open-source toolkit for facilitating research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are powerful tools that can understand and generate human-like text. However, they also raise concerns about privacy and security because they don’t recognize individual identities. To address these issues, scientists propose a new way to embed a secret code into text without sharing the original message. This “watermark” is then extracted by other experts to verify the identity of the LLM. The researchers suggest a new method for embedding watermarks that keeps sensitive information private and increases accuracy in identifying different LLMs. |
Keywords
» Artificial intelligence » Embedding
