Memorization in In-Context Learning (2408.11546v3)

Abstract: In-context learning (ICL) has proven to be an effective strategy for improving the performance of LLMs with no additional training. However, the exact mechanism behind this performance improvement remains unclear. This study is the first to show how ICL surfaces memorized training data and to explore the correlation between this memorization and performance on downstream tasks across various ICL regimes: zero-shot, few-shot, and many-shot. Our most notable findings include: (1) ICL significantly surfaces memorization compared to zero-shot learning in most cases; (2) demonstrations, without their labels, are the most effective element in surfacing memorization; (3) ICL improves performance when the surfaced memorization in few-shot regimes reaches a high level (about 40%); and (4) there is a very strong correlation between performance and memorization in ICL when it outperforms zero-shot learning. Overall, our study uncovers memorization as a new factor impacting ICL, raising an important question: to what extent do LLMs truly generalize from demonstrations in ICL, and how much of their success is due to memorization?

• The paper demonstrates that even as few as 25-shot demonstrations trigger marked memorization, enhancing model performance.
• The study reveals that segment pairs alone activate memorized data, challenging the assumed critical role of labels in ICL.
• Empirical results show a strong correlation between memorized content and task performance, suggesting memorization is key in successful ICL.

Memorization in In-Context Learning: An Analysis of the Impact on Performance

The paper titled "Memorization in In-Context Learning" offers a detailed analysis of in-context learning (ICL) and its intricate relationship with memorization within LLMs. The primary focus is to ascertain how ICL enhances performance by potentially leveraging memorized data from pre-existing training sets.

Key Findings

1. Memorization Surfaces with Increased Demonstrations: The paper establishes that the inclusion of demonstrations in ICL prompts significantly increases the surfacing of memorized content. Notably, even with as few as 25-shot demonstrations, a marked enhancement in the recall of training data is observed. Such memorization emerges prominently in ICL compared to zero-shot learning strategies.
2. Segment Pairs as Catalysts: Among the elements contributing to memorization, segment pairs (demonstrations excluding labels) play a crucial role. This discovery challenges existing assumptions that labels might significantly contribute to ICL success. Instead, segment pairs alone are sufficiently potent in activating memorized knowledge.
3. Memorization-Performance Correlation: There is a strong correlation between surfaced memorization and improved task performance, particularly when ICL surpasses the zero-shot counterparts. The paper quantifies this relationship across various datasets and scenarios using Pearson correlation coefficients, underscoring memorization as a pivotal variable.
4. Stability Across Many-Shot Regimes: While memorization stabilizes in many-shot regimes, exact matches gradually increase relative to near-exact matches, indicating a transformation towards more explicit memorization.
5. Trivial Role of Labels: Observations reveal that labels contribute minimally to performance enhancement in ICL, aligning with findings from related studies which emphasize that performance does not suffer significantly with randomized labels.

Implications

• Theoretical Exploration: The insights from this research contribute notably to the understanding of ICL by exposing memorization as a significant driver of its performance gains. This raises questions about the extent of true generalization versus reliance on memorized data, providing a refreshing perspective on the cognitive processes of LLMs.
• Practical Applications: For practitioners of machine learning and AI development, appreciating the dynamics of memorization could inform strategies for evaluating model outputs, especially in contexts where uninterpreted memorization might be either beneficial or detrimental. Understanding these dynamics can help refine approaches that rely on ICL for improved model performance.
• Future Research Directions: Future inquiries might delve into mechanisms that explicitly distinguish between memorization and true learning in LLMs. Additionally, leveraging findings on the non-essential nature of labels could refine prompt engineering methodologies, potentially simplifying designs for future LLM architectures.

Conclusion

This paper highlights memorization as a pivotal aspect in the field of in-context learning, presenting a substantive correlation with improved model performance. By unveiling the critical role of demonstrations—without accompanying labels—the paper challenges prevailing paradigms about what constitutes effective learning within LLMs. The insights compel researchers to re-evaluate how models internalize and utilize training data, offering a foundation for enhanced ICL strategies in AI advancements.