arXiv AI
Oct 1

Mitigating Memorization In Language Models

The paper explores ways to reduce the memorization of training data in language models, testing three regularizer-based, three finetuning-based, and eleven machine unlearning methods—five of which are newly introduced. It introduces TinyMem, a lightweight suite of small models for rapid testing of these mitigation techniques, and shows that unlearning methods, particularly BalancedSubnet, outperform others in removing memorized content while maintaining task performance. The study also finds that regularizer-based approaches are slow and ineffective, while finetuning methods are costly, especially when high accuracy is required.

By Mansi Sakarvadia, Aswathy Ajith, Arham Khan, Nathaniel Hudson, Caleb Geniesse, Kyle Chard, Yaoqing Yang, Ian Foster, Michael W. Mahoney
arXiv Machine Learning
Oct 16

Mitigating Memorization Where It Happens

The paper introduces a token‑level log‑likelihood ratio to pinpoint where large language models memorize training sequences. It proposes a probe‑steer framework, instantiated as Gated Subspace Steering (GSS), that selectively corrects memorization‑relevant activations using the leading singular vectors of a gradient‑weighted activation matrix. Experiments on four benchmarks show GSS matches or surpasses state‑of‑the‑art memorization reduction methods while using 10–100× less compute.

By Xuanqi Zhang, Haoyang Shang, Xiaoxiao Li
arXiv Machine Learning
Sep 11

Rebalancing Token Importance in Language Models with TF-IDF Weighted Cross-Entropy Loss

The paper proposes an information-weighted cross‑entropy loss that rescales token contributions using TF‑IDF statistics, thereby emphasizing semantically informative tokens and down‑weighting ubiquitous ones. Experiments on five decoder‑only language models (1.1B–13B parameters) show consistent reductions in memorized substring length while maintaining perplexity and downstream performance. The method is architecture‑agnostic, adds less than 3% computational overhead, and can be integrated into existing training pipelines.

By Zhijian Li, Stefan Larson, Kevin Leach