Summary of A Multi-perspective Analysis Of Memorization in Large Language Models, by Bowen Chen et al.
A Multi-Perspective Analysis of Memorization in Large Language Models
by Bowen Chen, Namgi Han, Yusuke Miyao
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper explores the memorization behavior of Large Language Models (LLMs), which exhibit impressive performance in various fields. Researchers have noticed that LLMs can generate the same content used for training, prompting a deeper understanding of this phenomenon. The study provides a comprehensive analysis of memorization from multiple perspectives, examining not only memorized but also less and unmemorized content. The results show a relationship between model size, continuation size, and context size, as well as the dynamics of generating memorized sentences. Additionally, embedding analysis reveals the distribution and decoding dynamics across model sizes for sentences with varying memorization scores. N-gram statistics analysis identifies a boundary effect when models start generating memorized or unmemorized sentences. Furthermore, the study demonstrates that it is possible to predict memorization by context using a Transformer model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are incredibly smart computers that can do many things well. Researchers have found that these LLMs can sometimes remember specific information they were trained on. This is called “memorization”. The paper talks about why this happens and what it looks like when LLMs generate memorized sentences versus new ones. It also shows how different-sized models behave in terms of memorization, and how we can predict what kind of content an LLM will generate based on its context. |
Keywords
» Artificial intelligence » Embedding » N gram » Prompting » Transformer
