Summary of Towards Operationalizing Right to Data Protection, by Abhinav Java et al.
Towards Operationalizing Right to Data Protection
by Abhinav Java, Simra Shahid, Chirag Agarwal
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 The abstract discusses the ethical concerns surrounding the use of language models (LMs) and proposes a new framework called RegText to generate unlearnable natural language datasets. This is achieved by injecting imperceptible spurious correlations into existing datasets, making them difficult for LMs to learn without affecting semantic content. The authors demonstrate the effectiveness of RegText through empirical analysis of small and large LMs, including GPT-4o and Llama. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where language models can’t learn from certain text data because it’s been made “unlearnable”! A team of researchers has developed a way to do just that with their new framework called RegText. They’re doing this by adding tiny, imperceptible changes to natural language datasets, making them harder for language models to understand without changing what the text actually means. The goal is to protect public data from being used in ways that might be unethical or illegal. |
Keywords
» Artificial intelligence » Gpt » Llama
