Recent language models, such as GPT -3+ [Brown et al., 2020, Achiam et al., 2023], have demonstrated Recent attempts to understand in-context learning have focused on various aspects. On the practical side, research has investigated the impact of different factors on in-context learning.

In table 3, we demonstrate the prompt for textualized recaptioning.

Transformers learn to approximate second-order optimization methods for ICL.

We further design an expert-wise training strategy to alleviate the impact of unhelpful demonstrations when optimizing the retriever model.

LLMs excel at few-shot in-context learning (ICL) - learning from a few input-output examples ("shots") provided in context at inference, without any weight updates.

Visual In-Context Learning (VICL) is a prevailing way to transfer visual foundation models to new tasks by leveraging contextual information contained in in-context examples to enhance learning and prediction of query samples.

Recently large language models like GPT -3 have shown encouraging performance as implicit knowledge source and revealed planning abilities.