Large language models (LLMs) have demonstrated remarkable capabilities in understanding and generating natural language data.

While recent research suggests that current large Vision-Language Models (VLMs) exhibit more reliance on shape, we find them to still be seriously limited in this regard.

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.

In-context learning (ICL) is one of the most powerful and most unexpected capabilities to emerge in recent transformer-based large language models (LLMs).

Pre-trained large language models (LLMs) based on Transformer have demonstrated striking in-context learning (ICL) abilities.

Chain-of-thought (CoT) is a method that enables language models to handle complex reasoning tasks by decomposing them into simpler steps.

Firstly, many existing attribution techniques require either computing the gradients [58] or multiple queries to the model [19], both of which are slow and computationally expensive. In contrast, ICL is often applied inreal-time to a large foundation model [12] that necessitates the attribution approaches for ICL to be fast and efficient.