The rapid advancement of large language models (LLMs) in recent years has made it feasible to establish domain-specific LLMs for specialized fields. However, in practical development, acquiring domain-specific knowledge often requires a significant amount of professional expert manpower. Moreover, even when domain-specific data is available, the lack of a unified methodology for benchmark dataset establishment often results in uneven data distribution. This imbalance can lead to an inaccurate assessment of the true model capabilities during the evaluation of domain-specific LLMs. To address these challenges, we introduce **SDBench**, a generic framework for generating evaluation datasets for domain-specific LLMs. This method is also applicable for establishing the LLM instruction datasets. It significantly reduces the reliance on expert manpower while ensuring that the collected data is uniformly distributed. To validate the effectiveness of this framework, we also present the **BridgeBench**, a novel benchmark for bridge engineering knowledge, and the **BridgeGPT**, the first LLM specialized in bridge engineering, which can solve bridge engineering tasks.

This paper proposes EmojiCloud, an open-source Python-based emoji cloud visualization tool, to generate a quick and straightforward understanding of emojis from the perspective of frequency and importance. EmojiCloud is flexible enough to support diverse drawing shapes, such as rectangles, ellipses, and image masked canvases. We also follow inclusive and personalized design principles to cover the unique emoji designs from seven emoji vendors (e.g., Twitter, Apple, and Windows) and allow users to customize plotted emojis and background colors. We hope EmojiCloud can benefit the whole emoji community due to its flexibility, inclusiveness, and customizability.