REALM: A Dataset of Real-World LLM Use Cases

24 Mar 2025 · Jingwen Cheng, Kshitish Ghate, Wenyue Hua, William Yang Wang, Hong Shen, Fei Fang ·

Large Language Models, such as the GPT series, have driven significant industrial applications, leading to economic and societal transformations. However, a comprehensive understanding of their real-world applications remains limited. To address this, we introduce REALM, a dataset of over 94,000 LLM use cases collected from Reddit and news articles. REALM captures two key dimensions: the diverse applications of LLMs and the demographics of their users. It categorizes LLM applications and explores how users' occupations relate to the types of applications they use. By integrating real-world data, REALM offers insights into LLM adoption across different domains, providing a foundation for future research on their evolving societal roles. A dedicated dashboard https://realm-e7682.web.app/ presents the data.

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Adam • Attention Dropout • BPE • Cosine Annealing • Dense Connections • Discriminative Fine-Tuning • Dropout • GELU • GPT • Layer Normalization • Linear Layer • Linear Warmup With Cosine Annealing • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • Softmax • Weight Decay

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REALM: A Dataset of Real-World LLM Use Cases

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