Summary of Measuring Copyright Risks Of Large Language Model Via Partial Information Probing, by Weijie Zhao et al.
Measuring Copyright Risks of Large Language Model via Partial Information Probing
by Weijie Zhao, Huajie Shao, Zhaozhuo Xu, Suzhen Duan, Denghui Zhang
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 Medium Difficulty summary: This paper explores the capacity of Large Language Models (LLMs) to generate infringing content by providing them with partial information from copyrighted materials. The authors investigate whether LLMs can be prompted to produce more infringing content through iterative prompting. Specifically, they input a portion of a copyrighted text into LLMs, prompt them to complete it, and analyze the overlap between the generated content and the original copyrighted material. The study demonstrates that LLMs can generate content highly overlapping with copyrighted materials based on these partial inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research looks at how well Large Language Models (LLMs) can create new content that is similar to existing, copyrighted work. The scientists tested if they could get LLMs to make more infringing content by giving them a little bit of the original text and asking them to finish it. They found out that LLMs can indeed create content that’s very close to the original material. |
Keywords
» Artificial intelligence » Prompt » Prompting
