2025
Disentangling Latent Shifts of In-Context Learning with Weak Supervision
NeurIPS 2025poster
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 o…