Self-Improving In-Context Learning

• URL: http://arxiv.org/abs/2605.23180v1
• Date: Fri, 22 May 2026 03:01:34 GMT
• Title: Self-Improving In-Context Learning
• Abstract summary: We propose to improve in-context learning by optimizing the embeddings of a few prompt test at time.<n>We formalize this signal as a bounded, self-supervised calibration procedure.<n>Across a comprehensive suite of ICL tasks, the proposed calibration improves or matches upon the base model and consistently outperforms classification-specific baselines on most tasks.
• Score: 3.9202238580555417
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose to improve in-context learning (ICL) by optimizing the continuous embeddings of a fixed few-shot prompt at test time. The key observation is that the log-probabilities a model assigns to its demonstrated outputs$\unicode{x2013}$available from a single forward pass without generating any tokens$\unicode{x2013}$provide a meaningful signal for how well the model has inferred the task from its demonstrations. We formalize this signal as a bounded, self-supervised confidence proxy and maximize it via zeroth-order optimization over the prompt embeddings, yielding a test-time calibration procedure. The approach requires no finetuning, no token generation, no predefined label set, and no external data, making it equally applicable to both classification and free-form generation tasks. Across a comprehensive suite of ICL tasks, the proposed calibration consistently matches or improves upon the base model and outperforms classification-specific baselines on most tasks. The statistically significant correlation between proxy improvement and downstream accuracy gain confirms that the proposed proxy encodes a reliable optimization signal for in-context learning.

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