Test-Time Adaptation via Many-Shot Prompting: Benefits, Limits, and Pitfalls
Test-time adaptation enables large language models (LLMs) to modify their behavior at inference without updating model parameters. A common approach is many-shot prompting, where large numbers of in-context learning (ICL) examples are injected as an input-space test-time update. Although performance can improve as more demonstrations are added, the reliability and limits of this update mechanism remain poorly understood, particularly for open-source models. We present an empirical study of many-shot prompting across tasks and model backbones, analyzing how performance varies with update magnitude, example ordering, and selection policy. We further study Dynamic and Reinforced ICL as alter...
| Authors | Shubhangi Upasani, Chen Wu, Jay Rainton et al. |
|---|---|
| Primary category | cs.LG |
| Published | 6 March 2026 |
| Last updated at source | 17 March 2026 |
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