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This research aims to explore how retraction tags can mitigate the issue of self-conditioning in autoregressive language models.

July 23, 2026Open Access

STRIKETHROUGH: In-Context Retraction Tags Mitigate Self-Conditioning in Language Models

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This research aims to explore how retraction tags can mitigate the issue of self-conditioning in autoregressive language models.
Controlled running-sum task with injected error rate.
Ablation study to isolate the effect of retraction tags versus answer hints.
Recall probe assessment to measure information retention with and without retraction tags.
Self-conditioning was observed in smaller models (Gemma-3 4B, Qwen-2.5 7B).
Using a retraction tag significantly improves recall by approximately 16x compared to deletion.
The effect of the retraction tag increased with model capability, almost eliminating self-conditioning in the 7B model.

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Randomized trial examines retraction tags' effect on self-conditioning in language models, suggesting improved recall.

Akshat Mishra (2026) studied this question.

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