Current approaches to mitigate these representational harms balance the number of retrieved items across population groups defined by a small number of (often binary) attributes. However, most existing methods overlook intersectional groups determined by combinations of group attributes, such as gender, race, and ethnicity.

Consequently, it is uncertain whether the generated modalities sufficiently represent the diversity of future behaviors.

To ensure quality, all charts and questions are handpicked, curated, and verified by human experts.

Large Language Models (LLMs) are widely used in today's tasks of natural language processing.

We leverage this unique capability to propose NoMAD-Attention, an efficient attention algorithm that replaces MAD operations with in-register lookups.

Inspired by the above motivation, we collect real cunning texts as our raw data from a famous Chinese online forum, the "Ruozhiba" (retard forum)

Authors are listed in alphabetical order.

Training models for ultra-high-resolution image generation presents significant challenges.

To ensure large language models (LLMs) are used safely, one must reduce their propensity to hallucinate or to generate unacceptable answers. A simple and often used strategy is to first let the LLM generate multiple hypotheses and then employ a reranker to choose the best one.