Summary of Model Stealing For Any Low-rank Language Model, by Allen Liu et al.
Model Stealing for Any Low-Rank Language Model
by Allen Liu, Ankur Moitra
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML)
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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 proposed research tackles the pressing issue of model stealing in machine learning, focusing on large language models (LLMs). The authors aim to establish a theoretical foundation for understanding this problem by investigating a simplified setting involving Hidden Markov Models (HMMs) and low-rank language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to figure out how someone else’s secret language model works. That’s what “model stealing” is all about! It’s an important problem because it could let people steal others’ ideas or even access private data. This paper looks at a special kind of model stealing that targets large language models, which are super powerful and useful for tasks like text analysis. The researchers want to understand how this works by studying simpler models called Hidden Markov Models (HMMs) and similar low-rank language models. |
Keywords
* Artificial intelligence * Language model * Machine learning
