Summary of Learning to Watermark Llm-generated Text Via Reinforcement Learning, by Xiaojun Xu et al.
Learning to Watermark LLM-generated Text via Reinforcement Learning
by Xiaojun Xu, Yuanshun Yao, Yang Liu
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper presents a novel approach to watermarking large language model (LLM) outputs, allowing for algorithmically detectable signals to be embedded into generated text to track misuse. The proposed method expands the watermark design space by incorporating the LLM tuning stage into the watermark pipeline. Unlike previous token-level watermark methods, this study designs a model-level watermark that embeds signals into LLM weights, which can be detected by a paired detector. A co-training framework based on reinforcement learning is introduced, iteratively training a detector to detect generated watermarked text and tuning the LLM to generate text easily detectable by the detector while maintaining normal utility. The results show that the proposed watermarks are more accurate, robust, and adaptable to new attacks. Additionally, open-sourcing the watermarked model is feasible. The study’s findings have implications for broader watermark design efforts, not limited to working with a fixed LLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create a way to add invisible signals to text generated by large language models (LLMs). This allows people to track if someone is using the model in an unfair or unauthorized way. The new approach looks at the entire model, not just individual words, and makes it easier to detect when something is fake. A special training process is used to make sure the signals are strong enough to be detected. The results show that this method works well and can help prevent misuse. It also makes it possible to open-source the watermarked model. |
Keywords
* Artificial intelligence * Large language model * Reinforcement learning * Token
