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The Effectiveness and Durability of Pretraining Data Filtering in Protein Language Models

Aaron Maiwald, Roderick McNeill, 이재영, Matthew A. Clarke, Lennart J. Justen, Alejandro Acelas, Ivo Andrews, Giles Harper-Donnelly, Adam Moore, Sophie Rose, Oliver M. Crook, Felix Jackson · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26651 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

プレトレーニングデータのフィルタリングは、オープンワイヤー言語モデルの最も一般的なセキュリティの1つです。ここでは、ESM-2ファミリーのタンパク質言語モデルを使用してその有効性、耐久性、スケーラビリティを研究します。私たちは、プレトレーニングデータからのウイルスタンパク質のフィルタリングは、マスクされた言語モデリング、変異効果予測、ウイルスタンパク質の長距離接触予測を有意に悪化させますが、非ウイルスタンパク質のパフォーマンスを大幅に維持しています。これらの効果は、ヒト感染ウイルスからのタンパク質に集中しており、私たちが8Mから3Bのパラメータのモデルをスケールする

Machine translation (Meta M2M100); the English original follows.

Abstract

Pretraining filtering is one of the most common safeguards for open-weight language models. Here we study its effectiveness, durability and scalability using the ESM-2 family of protein language models. We find that filtering viral proteins from the pretraining data meaningfully degrades masked language modelling, mutation effect prediction and long-range contact prediction on viral proteins, while largely preserving performance on non-viral proteins. These effects are concentrated among proteins from human-infecting viruses, and grow as we scale models from 8M to 3B parameters. As models increase in size, we find that stringent filtering becomes more effective. However, fine-tuning on sequences from the removed viral families restores the lost performance. Although the cost to recover performance increases with model scale, even for the largest models it costs about 80 USD of H100 time. This work establishes pretraining filtering as moderately effective at selectively degrading performance on viral proteins, but not durable against fine-tuning with modest computational resources and access to relevant data.

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Topics: Scalability, Computer science, Language model, Durability, Artificial intelligence, Scale (ratio)

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