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ROME: Memorization Insights from Text, Logits and Representation (2403.00510v3)

Published 1 Mar 2024 in cs.CL and cs.AI

Abstract: Previous works have evaluated memorization by comparing model outputs with training corpora, examining how factors such as data duplication, model size, and prompt length influence memorization. However, analyzing these extensive training corpora is highly time-consuming. To address this challenge, this paper proposes an innovative approach named ROME that bypasses direct processing of the training data. Specifically, we select datasets categorized into three distinct types -- context-independent, conventional, and factual -- and redefine memorization as the ability to produce correct answers under these conditions. Our analysis then focuses on disparities between memorized and non-memorized samples by examining the logits and representations of generated texts. Experimental findings reveal that longer words are less likely to be memorized, higher confidence correlates with greater memorization, and representations of the same concepts are more similar across different contexts. Our code and data will be publicly available when the paper is accepted.

PDF HTML Abstract

Exploring Memorization in LLMs Without Access to Training Data

Introduction

The exploration of memorization in LLMs pivots on understanding how these models store and reproduce information from their vast training corpora. Traditionally, this investigation has relied on direct comparisons between models' outputs and their training data. This approach not only poses practical challenges due to the colossal size of the training sets but also raises privacy and security concerns. This paper introduces a novel approach named ROME, which aims to explore memorization through constructed memorized and non-memorized samples without requiring direct access to the training data. By leveraging datasets designed to probe LLMs and analyzing memorization through text, probability, and hidden state perspectives, the work presents new empirical findings that contribute to our understanding of how memorization operates in billion-scale LLMs.

Methodology

ROME's methodology circumvents the necessity of direct comparison with training data by using specially selected datasets—IDIOMIM and CelebrityParent. These datasets facilitate the examination of model outputs in terms of text completion and reversal relations, enabling the categorization of responses into memorized and non-memorized based on their match with predefined correct answers. This binary classification lays the groundwork for a detailed analysis across three dimensions:

  • Text: Comparison based on linguistic and statistical features such as word length and part-of-speech.
  • Probability: Analysis of the likelihood distributions associated with generated tokens.
  • Hidden State: Examination of the model's internal representations for input and output tokens.

This framework allows for a nuanced exploration of how different factors influence memorization in LLMs, without the inherent limitations and complexities of accessing models' training data.

Experimental Results

The paper revealed several key insights into the mechanisms of memorization in LLMs:

  • Longer prompts and idioms tend to be more memorized, suggesting that additional context supports recall.
  • Contrary to some existing theories, longer words within idioms showed a decreased likelihood of memorization.
  • Analysis by part-of-speech indicated that nouns have moderate memorization rates, while adverbs and adpositions are more likely to be memorized.
  • From a probabilistic perspective, memorized samples demonstrated greater mean probabilities and reduced variance compared to non-memorized samples.
  • Hidden state analysis suggested that memorized samples exhibit smaller means and variances, challenging previous assumptions about the relationship between word frequency and memorization.

Practical and Theoretical Implications

The findings from this paper have both practical and theoretical implications for the development and understanding of LLMs. Practically, the insights into how the length of prompts, the complexity of words, and the statistical properties of model outputs affect memorization can inform the design and training of more efficient and less predictable models. Theoretically, the observation that memorization mechanisms in LLMs can be reliably analyzed without direct access to training data opens new avenues for research, particularly in probing the depth of models' understanding and their reliance on surface-level patterns versus deeper semantic processing.

Future Directions

Considering the limitations outlined, future work could explore additional datasets and model architectures, incorporate more nuanced categorizations of memorization, and seek to establish clearer causal relationships between the observed phenomena and underlying model characteristics. Moreover, extending the methodology to models trained on multilingual or domain-specific corpora could yield further insights into the generality and specificity of memorization processes.

Conclusion

This paper presents a meaningful advance in the paper of memorization in LLMs by demonstrating a viable approach to probing models' recall capabilities without direct access to their vast training datasets. Through meticulous analysis across text, probability, and hidden state dimensions, this work not only broadens our understanding of memorization mechanisms but also poses intriguing questions for future exploration. As LLMs continue to grow in size and sophistication, methodologies like ROME will be invaluable in ensuring these models remain interpretable, secure, and aligned with human values.

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Authors (3)
  1. Bo Li (1107 papers)
  2. Qinghua Zhao (26 papers)
  3. Lijie Wen (58 papers)
Citations (1)
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