AINeutralarXiv – CS AI · 6h ago6/10
🧠
Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval
Researchers propose DOPA, a demonstration retrieval framework that uses out-of-distribution proxies to improve large language model performance on tasks from inaccessible target domains. The method combines proxy-based evaluation with diversity constraints to enhance LLM robustness when facing severe distribution shifts.