Summary of Understanding Privacy Risks Of Embeddings Induced by Large Language Models, By Zhihao Zhu et al.
Understanding Privacy Risks of Embeddings Induced by Large Language Models
by Zhihao Zhu, Ninglu Shao, Defu Lian, Chenwang Wu, Zheng Liu, Yi Yang, Enhong Chen
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Large language models (LLMs) have shown early signs of artificial general intelligence but struggle with hallucinations. To mitigate these issues, one promising solution is to store external knowledge as embeddings, aiding LLMs in retrieval-augmented generation. However, this approach risks compromising privacy, as recent studies have experimentally demonstrated that original text can be partially reconstructed from text embeddings by pre-trained language models. Despite the significant advantage of LLMs over traditional pre-trained models, their widespread use may exacerbate these concerns. This paper investigates the effectiveness of reconstructing original knowledge and predicting entity attributes from these embeddings when LLMs are employed. The results show that LLMs significantly improve accuracy in two evaluated tasks over pre-trained models, regardless of whether texts are in-distribution or out-of-distribution. This highlights the potential for LLMs to jeopardize user privacy, emphasizing the negative consequences of their widespread use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are getting really good at understanding human language, but they sometimes make things up that aren’t true. To help them be more accurate, some people suggest storing extra information as special codes called embeddings. However, this idea might have a bad side effect: it could allow someone to figure out what the original text said from these special codes. This is a big concern because LLMs are getting better and better at understanding language, which means they could be used to hurt people’s privacy. In this study, researchers tested whether LLMs can reconstruct original information and guess details about things mentioned in texts. They found that LLMs do a much better job than older models at doing these tasks, both when the text is familiar or not. This has important implications for how we use LLMs in the future. |
Keywords
» Artificial intelligence » Retrieval augmented generation
