Summary Quantifying and Analyzing Entity-level Memorization in Large Language Models arxiv.org
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This paper introduces an adaptive prompt approach to address the privacy concerns of large language models that can memorize training data, without the need for computationally expensive methods.
Slides
Slide Presentation (9 slides)
Key Points
- Large language models have the ability to memorize their training data, raising privacy concerns.
- Quantifying and analyzing memorization in language models is important for evaluating privacy risks.
- Existing methods for quantifying memorization are computationally expensive.
- The paper proposes a definition for entity-level memorization and introduces an approach for adaptive prompt learning.
- Soft prompts in large language models improve and stabilize as the dataset size increases, but decline in effectiveness with massive training datasets.
Summaries
30 word summary
Large language models (LLMs) can memorize training data, posing privacy concerns. Existing methods for quantifying memorization are computationally expensive. This paper defines entity-level memorization and presents an adaptive prompt approach.
38 word summary
Large language models (LLMs) have the ability to memorize training data, which raises privacy concerns. Existing methods for quantifying memorization are computationally expensive. This paper proposes a definition for entity-level memorization and introduces an approach for adaptive prompt
244 word summary
Large language models (LLMs) have the ability to memorize their training data, which raises privacy concerns. Quantifying and analyzing this memorization is important for evaluating privacy risks. However, existing methods for quantifying memorization are either computationally expensive
This paper addresses the challenges of quantifying and analyzing memorization in language models (LLMs) using textual prompts. The authors propose a definition for entity-level memorization and introduce an approach for adaptive prompt learning that utilizes entity attribute information and soft prompts.
Researchers have explored prompt-based approaches that use continuous vectors in the embedding space of language models to improve model performance. These approaches have yielded effective solutions for improving model performance. The volume of fabricated and real data used in entity extraction rate has an impact on accuracy
The effectiveness of soft prompts in large language models improves and stabilizes as the dataset size increases. However, with massive training datasets, the effectiveness declines and exhibits fluctuations. This may be because an abundance of training data causes the soft prompts to lose some of
This document discusses the quantification and analysis of entity-level memorization in large language models. The authors aim to explore entity-level memorization in models ranging from 50-200, 200-500, and 500-1000, and then
This text excerpt consists of a list of references to various papers and reports related to language models. These references include papers on topics such as privacy attacks on ChatGPT, optimizing continuous prompts for generation, surveying prompting methods in natural language processing, analyzing