Stochasticity in Tokenisation Improves Robustness

Sophie Steger, Rui Li, Sofiane Ennadir, Anya Sims, Arno Solin, Franz Pernkopf, Martin Trapp · Apr 17, 2026 · Citations: 0

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Abstract

The widespread adoption of large language models (LLMs) has increased concerns about their robustness. Vulnerabilities in perturbations of tokenisation of the input indicate that models trained with a deterministic canonical tokenisation can be brittle to adversarial attacks. Recent studies suggest that stochastic tokenisation can deliver internal representations that are less sensitive to perturbations. In this paper, we analyse how stochastic tokenisations affect robustness to adversarial attacks and random perturbations. We systematically study this over a range of learning regimes (pre-training, supervised fine-tuning, and in-context learning), data sets, and model architectures. We show that pre-training and fine-tuning with uniformly sampled stochastic tokenisations improve robustness to random and adversarial perturbations. Evaluating on uniformly sampled non-canonical tokenisations reduces the accuracy of a canonically trained Llama-1b model by 29.8%. We find that training with stochastic tokenisation preserves accuracy without increasing inference cost.

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What We Could Verify

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provisional (inferred)

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"The widespread adoption of large language models (LLMs) has increased concerns about their robustness."

Evaluation Modes

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Automatic metrics

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"The widespread adoption of large language models (LLMs) has increased concerns about their robustness."

Quality Controls

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"The widespread adoption of large language models (LLMs) has increased concerns about their robustness."

Benchmarks / Datasets

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"The widespread adoption of large language models (LLMs) has increased concerns about their robustness."

Reported Metrics

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Accuracy

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"Evaluating on uniformly sampled non-canonical tokenisations reduces the accuracy of a canonically trained Llama-1b model by 29.8%."

Rater Population

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"The widespread adoption of large language models (LLMs) has increased concerns about their robustness."

Human Feedback Details

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Potential metric signals: Accuracy
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Research Brief

Metadata summary

The widespread adoption of large language models (LLMs) has increased concerns about their robustness.

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Key Takeaways

The widespread adoption of large language models (LLMs) has increased concerns about their robustness.
Vulnerabilities in perturbations of tokenisation of the input indicate that models trained with a deterministic canonical tokenisation can be brittle to adversarial attacks.
Recent studies suggest that stochastic tokenisation can deliver internal representations that are less sensitive to perturbations.

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