Disentangling Latent Shifts of In-Context Learning with Weak Supervision

• URL: http://arxiv.org/abs/2410.01508v3
• Date: Fri, 24 Oct 2025 10:38:52 GMT
• Title: Disentangling Latent Shifts of In-Context Learning with Weak Supervision
• Authors: Josip Jukić, Jan Šnajder,
• Abstract summary: In-context learning (ICL) enables large language models to perform few-shot learning by conditioning on labeled examples in the prompt.<n>Despite its flexibility, ICL suffers from instability as prompt length increases with more demonstrations.<n>We propose a parameter-efficient method that disentangles demonstration-induced latent shifts from those of the query.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-context learning (ICL) enables large language models to perform few-shot learning by conditioning on labeled examples in the prompt. Despite its flexibility, ICL suffers from instability -- especially as prompt length increases with more demonstrations. To address this, we treat ICL as a source of weak supervision and propose a parameter-efficient method that disentangles demonstration-induced latent shifts from those of the query. An ICL-based teacher generates pseudo-labels on unlabeled queries, while a student predicts them using only the query input, updating a lightweight adapter. This captures demonstration effects in a compact, reusable form, enabling efficient inference while remaining composable with new demonstrations. Although trained on noisy teacher outputs, the student often outperforms its teacher through pseudo-label correction and coverage expansion, consistent with the weak-to-strong generalization effect. Empirically, our method improves generalization, stability, and efficiency across both in-domain and out-of-domain tasks, surpassing standard ICL and prior disentanglement methods.

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