  This paper explores the image-sharing capability of Large Language Models
(LLMs), such as GPT-4 and LLaMA 2, in a zero-shot setting. To facilitate a
comprehensive evaluation of LLMs, we introduce the PhotoChat++ dataset, which
includes enriched annotations (i.e., intent, triggering sentence, image
description, and salient information). Furthermore, we present the
gradient-free and extensible Decide, Describe, and Retrieve (DribeR) framework.
With extensive experiments, we unlock the image-sharing capability of DribeR
equipped with LLMs in zero-shot prompting, with ChatGPT achieving the best
performance. Our findings also reveal the emergent image-sharing ability in
LLMs under zero-shot conditions, validating the effectiveness of DribeR. We use
this framework to demonstrate its practicality and effectiveness in two
real-world scenarios: (1) human-bot interaction and (2) dataset augmentation.
To the best of our knowledge, this is the first study to assess the
image-sharing ability of various LLMs in a zero-shot setting. We make our
source code and dataset publicly available at
https://github.com/passing2961/DribeR.
