The field of large language models is moving towards more robust and efficient optimization methods. Researchers are exploring ways to improve the reliability and transferability of prompts, as well as reducing the computational costs associated with in-context learning. Distributionally robust optimization and meta-learning are emerging as key approaches to achieve these goals. Notably, innovative methods such as robust Bayesian optimization and layer-wise compression are being developed to address the challenges of prompt optimization and many-shot learning.

Some particularly noteworthy papers in this area include: DRO-InstructZero, which formulates zero-shot prompt optimization as robust Bayesian optimization to improve reliability under distribution shift. MemCom, which proposes a layer-wise compression method to improve the memory and computational efficiency of in-context learning. VIPAMIN, which introduces a visual prompt initialization strategy to enhance adaptation of self-supervised models. CaPT, which leverages instance-aware information to enhance prompt-tuned model performance without additional fine-tuning. PROMPT-MII, which proposes a reinforcement learning-based framework to meta-learn an instruction induction model that can generate compact instructions on the fly.