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Change Logs:

• 2023-10-18: First draft. This paper appears at EMNLP 2024. This paper is a work by John X. Morris. It comes with an easy-to-use library that could revert the OpenAI embeddings.

Overview

The authors assume an attacker has access to (1) a compromised vector database, and (2) a black-box embedding model \phi(\cdot) (for example, OpenAI’s embedding API). The attacker starts from an embedding and an empty string to reconstruct the original text corresponding to that string; the method proposed in the paper manage to recover a string up to 32 tokens.

The main motivation of this paper is privacy.

Method

Reference

1. [2211.00053] Generating Sequences by Learning to Self-Correct (Welleck et al.): This is the main inspiration of the main paper.

   This method relates to other recent work generating text through iterative editing (Lee et al., 2018; Ghazvininejad et al., 2019). Especially relevant
   is Welleck et al. (2022), which proposes to train a text-to-text ‘self-correction’ module to improve language model generations with feedback.

2. Decoding a Neural Retriever’s Latent Space for Query Suggestion (Adolphs et al., EMNLP 2022)