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Contrast Matters: Understanding Robustness of In-Context Fine-Tuning to Target-Context Relatedness
In-context fine-tuning (IC-Train), training an LLM with labeled examples in-context, is increasingly used in place of standard fine-tuning for domain adaptation and continual absorption of labeled data. We study the robustness of the in-context learning ability that emerges from such training: does
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