Remote Sensing Image Change Captioning (RSICC) faces significant challenges in effectively identifying and articulating changes between bi-temporal images. Traditional approaches often utilize individual text decoders, which may not capture the subtleties of visual changes nor fully exploit advanced language modeling capabilities. To overcome these limitations, we propose Change-Aware Adaption, namely Chareption, a novel framework that effectively leverages pre-trained large language models (LLMs) to enhance both the accuracy and detail of change captions. Central to Chareption is a change-aware module designed to selectively identify and utilize tokens that significantly represent changes, thus avoiding the common issue of redundancy that plagues methods relying solely on class tokens or indiscriminate use of all patch tokens. Additionally, Chareption designs a lightweight change adapter module, seamlessly integrated into both the vision backbone and the LLM, requiring minimal learnable parameters while optimally adjusting representations for the RSICC task. Our experiments on the LEVIR-CC dataset demonstrate that Chareption significantly outperforms existing methods in caption accuracy and contextual relevance, while also reducing training overhead. This establishes Chareption as a pioneering solution that sets a new direction in RSICC by harnessing the rich representational power of LLMs for improved multimodal understanding.