Papers by Mingshi Xu

2 papers
Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence (2023.findings-acl)

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Challenge: Sentence-level representations are beneficial for various natural language processing tasks.
Approach: They propose a generative embedding inversion attack that reconstructs input sequences based only on their sentence embeddeds.
Outcome: The proposed model outperforms previous embedding inversion attacks in classification metrics and generates coherent and contextually similar sentences as original inputs.
Multi-step Jailbreaking Privacy Attacks on ChatGPT (2023.findings-emnlp)

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Challenge: With the rapid evolution of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts.
Approach: They propose to integrate ChatGPT and Bing GPT3 into their applications to create a set of LLMs that can be used to generate NLP tasks with appropriate prompts.
Outcome: The proposed models can be zero-shot or few-shot learners to solve specified tasks and can even be zero or few shot learners.

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