Large language models (LLMs) are increasingly powerful, but aligning them with human values and perspectives remains a challenge, especially for subjective tasks like identifying hate speech. Imagine trying to teach an AI to understand nuances of language, cultural context, and differing opinions – it's like trying to nail jelly to a wall. A new Python library called SubData aims to make this process easier by providing researchers with a powerful toolkit for collecting, combining, and utilizing datasets specifically designed to evaluate LLM alignment on subjective downstream tasks. Why is this important? Because subjective tasks often matter most in the real world. Think about content moderation: what one person considers offensive, another might find acceptable. LLMs need to navigate these grey areas effectively. SubData helps by providing access to a diverse range of datasets, initially focusing on hate speech detection, allowing researchers to test how well LLMs align with different perspectives on what constitutes hateful content. For example, does an LLM trained on one demographic's perspective classify hate speech differently than one trained on another's? SubData empowers researchers to ask these questions. The library's key innovation is its ability to combine instances from various datasets, streamlining the process of creating specialized resources. This is coupled with a mapping system that standardizes target terminology and a taxonomy to categorize those targets, allowing for more consistent analysis. Importantly, SubData is designed for flexibility. Researchers can customize both the mapping and taxonomy to align with their specific research goals. While initially focused on hate speech, SubData’s creators envision expanding it to other subjective constructs like misinformation, creating a versatile benchmark suite for evaluating LLM alignment across a range of real-world applications. This will be crucial as AI takes on ever more complex and nuanced tasks, requiring alignment not just with general human values, but also with the diverse perspectives found within our global society. This project is a big step forward in building more responsible and truly helpful AI.