Summary of Amplifying Training Data Exposure Through Fine-tuning with Pseudo-labeled Memberships, by Myung Gyo Oh et al.
Amplifying Training Data Exposure through Fine-Tuning with Pseudo-Labeled Memberships
by Myung Gyo Oh, Hong Eun Ahn, Leo Hyun Park, Taekyoung Kwon
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 attack scenario against neural language models (LMs) that are vulnerable to data memorization. The attacker fine-tunes pre-trained LMs to amplify the exposure of the original training data, achieving remarkable results with large-scale models having over 1 billion parameters. To quantify the amount of pre-training data within generated texts, the authors propose using pseudo-labels based on membership approximations from the target LM. This approach enables the attacker to favor generations with higher likelihoods of originating from the pre-training data. The study highlights the importance of addressing this vulnerability and suggests future research directions for mitigating these attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart computer program that can understand and generate human-like text. This program, called a neural language model (LM), is really good at learning from lots of text data. But, what if someone with bad intentions wants to find out the source of this training data? In this paper, researchers show how an attacker can trick the LM into revealing more information about its training data. The attacker does this by giving the LM new texts that are similar to the original data it learned from. By using special labels and probabilities from the LM itself, the attacker can make the LM reveal even more information. This study shows just how vulnerable these language models can be and suggests ways to make them safer. |
Keywords
* Artificial intelligence * Language model
