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

Abstract:Large language models are typically trained under uniform token weighting, which allows frequent and low-information tokens to dominate learning and can increase the tendency to memorize surface-level text spans. To address this, we present an information-weighted cross-entropy loss that rescales token-level contributions using TF-IDF statistics, emphasizing semantically informative tokens while down-weighting ubiquitous ones. Experiments on five decoder-only LLMs ranging from 1.1B to 13B parameters show consistent reductions in memorized substring length while preserving perplexity and downstream task performance. Under LoRA fine-tuning, TF-IDF reduces average substring memorization length by 14% across all five models. Under full-weight fine-tuning on TinyLLaMA 1.1B, the reduction reaches 58%. Our approach is architecture-agnostic and can be incorporated into existing training pipelines with less than 3% computational overhead, offering a lightweight and principled way to mitigate memorization without disrupting standard training dynamics.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.11029 [cs.CL]

(or arXiv:2609.11029v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.11029 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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