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Memory Dial: A Training Framework for Controllable Memorization in Language Models

arXiv:2604.05074v1 Announce Type: new Abstract: Memorization in language models is widely studied but remains difficult to isolate and control. Understanding when and what models memorize is essential for explaining their predictions, yet existing approaches are post-hoc: they can detect memorization in trained models, but cannot disentangle its effects from architecture, data, or optimization. We introduce Memory Dial, a training framework that makes memorization pressure an explicit, controllable variable. Memory Dial interpolates between standard cross-entropy and a temperature-sharpened objective via a single parameter $\alpha$, producing a family of models identical in architecture and training setup (within each sweep), differing only in memorization pressure. Experiments across six architectures and five benchmarks demonstrate that: (1) $\alpha$ reliably controls memorization pressure, with seen-example accuracy increasing monotonically while unseen accuracy remains stable; (2)

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Xiangbo Zhang, Ali Emami · · 1 min read · 4 views
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arXiv:2604.05074v1 Announce Type: new Abstract: Memorization in language models is widely studied but remains difficult to isolate and control. Understanding when and what models memorize is essential for explaining their predictions, yet existing approaches are post-hoc: they can detect memorization in trained models, but cannot disentangle its effects from architecture, data, or optimization. We introduce Memory Dial, a training framework that makes memorization pressure an explicit, controllable variable. Memory Dial interpolates between standard cross-entropy and a temperature-sharpened objective via a single parameter $\alpha$, producing a family of models identical in architecture and training setup (within each sweep), differing only in memorization pressure. Experiments across six architectures and five benchmarks demonstrate that: (1) $\alpha$ reliably controls memorization pressure, with seen-example accuracy increasing monotonically while unseen accuracy remains stable; (2) larger models are more responsive to memorization pressure; and (3) frequent sequences are easier to memorize than rare ones. Additional analyses show that the effect is robust across a range of sharpening temperatures, differs qualitatively from single-temperature cross-entropy, transfers to multilingual settings, and is detectable even on naturally occurring single-occurrence sequences. Memory Dial provides a controlled experimental framework for studying how memorization behavior emerges and interacts with generalization in language models.

Executive Summary

The article 'Memory Dial: A Training Framework for Controllable Memorization in Language Models' introduces a novel training framework, Memory Dial, designed to isolate and control memorization in language models (LMs). By interpolating between standard cross-entropy and a temperature-sharpened objective via a parameter α, the framework allows researchers to explicitly manipulate memorization pressure while maintaining consistent architecture and training conditions. Empirical evaluations across six architectures and five benchmarks reveal that α reliably modulates memorization pressure, with minimal impact on unseen accuracy. The study further demonstrates that larger models exhibit greater responsiveness to memorization pressure and that frequent sequences are more easily memorized than rare ones. The framework's robustness is validated across varying sharpening temperatures, multilingual settings, and even naturally occurring single-occurrence sequences, offering a controlled tool for dissecting the interplay between memorization and generalization in LMs.

Key Points

  • ▸ Memory Dial introduces a tunable parameter α to explicitly control memorization pressure in LMs, enabling clean experimental separation of memorization effects from architectural and training confounders.
  • ▸ Empirical results show that increasing α monotonically increases seen-example accuracy without degrading unseen performance, with larger models being more sensitive to memorization pressure and frequent sequences being easier to memorize.
  • ▸ The framework's robustness extends to diverse scenarios, including multilingual settings and naturally occurring single-occurrence sequences, highlighting its versatility for studying memorization dynamics.

Merits

Methodological Rigor

The Memory Dial framework addresses a critical gap in LM research by providing a controlled, parameterized approach to study memorization, untangling it from confounding factors such as architecture and data distribution.

Empirical Breadth

The study evaluates Memory Dial across six architectures and five benchmarks, demonstrating broad applicability and robustness, which strengthens the credibility of the findings.

Theoretical Insight

By decoupling memorization from generalization, the framework offers a new lens to explore fundamental questions about how LMs learn and retain information, contributing to both practical and theoretical advancements in the field.

Demerits

Limited Generalizability to Real-World Scenarios

While Memory Dial provides controlled conditions, its reliance on synthetic or curated datasets may not fully capture the complexities of real-world data distributions, potentially limiting the direct applicability of its findings.

Dependence on Temperature Parameter

The effectiveness of Memory Dial hinges on the choice of temperature in the sharpened objective. Suboptimal temperature settings could introduce noise or instability, undermining the framework's reliability.

Focus on Memorization Only

The study primarily examines memorization and its interaction with generalization but does not explore other critical aspects of LM behavior, such as reasoning or adaptability, which may limit the scope of its contributions.

Expert Commentary

The Memory Dial framework represents a significant advancement in the study of memorization in language models, offering a level of control and precision that has been lacking in prior research. By decoupling memorization from other confounding factors, the authors provide a robust experimental tool that can deepen our understanding of how LMs learn and retain information. The empirical findings, particularly the monotonic relationship between α and memorization pressure, are compelling and suggest that the framework could become a standard tool in LM research. However, the reliance on synthetic or curated datasets may limit the direct applicability of the results to real-world scenarios, where data distributions are far more complex and noisy. Furthermore, while the framework excels at isolating memorization, it does not address other critical aspects of LM behavior, such as reasoning or adaptability, which are equally important for building trustworthy AI systems. Nonetheless, Memory Dial opens new avenues for research and could play a pivotal role in shaping future studies on memorization, generalization, and the ethical implications of LMs.

Recommendations

  • ✓ Researchers should explore the integration of Memory Dial with other training techniques, such as regularization or data augmentation, to assess whether the framework can be used to simultaneously control memorization and improve generalization.
  • ✓ Future work should extend the evaluation of Memory Dial to more diverse and noisy real-world datasets, ensuring that the framework's findings are applicable to practical LM deployment scenarios.
  • ✓ Policymakers and industry stakeholders should collaborate to develop standardized protocols for using Memory Dial in AI governance frameworks, ensuring that the insights gained from the framework are translated into actionable guidelines for safe and responsible AI development.

Sources

Original: arXiv - cs.CL

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