Entropy-aware Masking for Masked Language Modeling

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, medium

Summary

Gokul Srinivasagan, Kai Hartung, and Munir Georges present "Entropy-aware Masking for Masked Language Modeling," a novel pretraining objective designed for encoder-based language models. This approach improves upon conventional random token masking by selecting tokens based on their entropy distribution, specifically targeting those deemed more informative and uncertain to enhance learning signals. The research also introduces a self-masking technique that boosts training efficiency by eliminating the need for an external reference model. Experimental evaluations show that this entropy-aware method achieves an average performance improvement of 5% in GLUE scores over baseline strategies. Furthermore, integrating knowledge distillation with entropy masking produced the most favorable overall results.

Key takeaway

For Machine Learning Engineers optimizing language model pretraining, consider adopting entropy-aware masking to enhance model performance. Your current random masking strategy might be suboptimal; switching to an entropy-based approach, especially with the proposed self-masking, can yield a 5% GLUE score improvement. Explore combining this technique with knowledge distillation for even better results, potentially streamlining your pretraining pipeline and improving downstream task performance.

Key insights

Masking tokens by entropy distribution improves language model pretraining efficacy.

Principles

• Target informative, uncertain tokens for masking.
• Entropy distribution guides token selection.
• Self-masking enhances training efficiency.

Method

Identify tokens for masking using the model's entropy over token predictions, then apply a novel self-masking approach, potentially combined with knowledge distillation.

In practice

• Implement entropy-based token selection.
• Integrate self-masking for efficiency.
• Combine with knowledge distillation.

Topics

• Masked Language Modeling
• Encoder-based Language Models
• Entropy Distribution
• Token Masking Strategies
• Knowledge Distillation
• GLUE Scores

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.