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 the fine-tuned model perform well across test inputs whose in-context examples range from unrelated to nearly identical? Across 32 configurations spanning four open-source LLMs and eight test sets over machine translation, Text-to-SQL, and multilingual semantic parsing, we show that robustness hinges on an overlooked design choice: how in-context examples are selected relative to the target dur...
| Authors | Deeptanshu Malu, Deevyanshu Malu, Aditya Nemiwal et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 April 2026 |
| Last updated at source | 27 September 2026 |
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