REALM : A Dataset of Real-World LLM Use Cases (2025)

Abstract: Large Language Models (LLMs), 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. An interactive dashboard ([https://realm-e7682.web.app/](https://realm-e7682.web.app/)) is provided for easy exploration of the dataset.

## introduction large language models (llms) have revolutionized ai with advanced natural language understanding and reasoning capabilities @xcite @xcite . these advancements have spurred new applications and transformed workflows across various industries. as llm capabilities expand, researchers in fields like social science, economics, and humancomputer interaction (hci) are examining their social implications, anticipating both benefits and disruptions, and addressing risks tied to their rapid adoption. currently, there remains limited understanding of how llms are deployed and utilized in realworld scenarios despite widespread adoption. existing studies generally fall into two primary categories. the first uses occupation-activity typologies to measure llm's influence on various tasks, aiming to assess task-exposure rates and impact on occupations @xcite @xcite . however, these studies often lack empirical grounding, weakening their validity. the second category analyzes large datasets from social media and other public sources to evaluate sentiment and trends around llm applications @xcite @xcite , but these studies are hindered by a lack of comprehensive taxonomies and rely on simplistic methods, such as keyword filtering, leading to low precision and recall. while some industry research (e.g., from linkedin, anthropic, goldman sachs) on llm impact is available, it is often not publicly accessible or prohibitively costly, limiting its use in academic research. to conduct an empirical study with a comprehensive taxonomy, we present a new dataset realworld applications of large language models (realm), documenting real-world use cases of llms. realm comprises over 94,000 instances, including 15,000 sourced from reddit discussions and 79,000 from news articles, spanning from june 2020 (the release date of gpt-3) to december 2024. our analysis focuses on two primary dimensions: the objectives of llm use case based on a comprehensive taxonomy @xcite , and the occupations of current or potential end-users, categorized using the o*net database @xcite . our contributions are threefold: (1) we employ a systematic and reliable taxonomy to link use cases to occupations (2) we provide initial statistical analyses derived from the dataset, demonstrating its potential for analyzing trends, informing policies, and supporting cross-domain research (3) we provide accessible via an interactive dashboard and an api for structured data processing. ## related work studying the impact of llm-based applications has emerged as a prominent research topic, including both structured analyses based on existing occupation-activity taxonomies and empirical investigations utilizing social media big data. some research examines the societal and economic impacts of llms through structured taxonomies, employing either gpt-based or human annotations to assess potential llm usage. for example, @xcite estimates that 19% of u.s. workers could have 50% or more of their tasks exposed to llms using a task-exposure rubric derived from the o*net database. this study builds on a broader body of work that quantifies ai exposure rates @xcite @xcite . however, these analyses often rely on predefined rubrics and expert opinions for annotation, which can introduce biases and fail to reflect real-world dynamics. in contrast to structured taxonomy-based approaches, empirical research leverages social media data to explore real-world applications of llms (naing and udomwong, 2024). for instance, @xcite analyzes sentiment trends and employs topic modeling to investigate the impacts of llms across domains such as education and technology, but utilizing a highly simplified taxonomy. @xcite and @xcite incorporate occupation-level analyses based on o*net and indeed to examine how different professions perceive and interact with llms. @xcite further analyzes userprovided prompts and applies topic modeling to understand user interactions with llms. however, these studies often depend on keyword filtering or hashtag-based data collection methods, which are imprecise and fail to provide a comprehensive taxonomy due to the necessity of omitting many keywords that may have multiple meanings. beyond social media, other research has examined llm adoption through alternative data sources with varying focuses, including scientific literature @xcite , large-scale user interaction logs with llms @xcite , and proprietary usage data from commercial llm deployments @xcite . the latter offers a comprehensive analysis of millions of conversations with claude, revealing correlations with user occupations, salaries, and job zones. however, their data is not publicly available, and the results building upon the aforementioned works, realm introduce a taxonomy-driven approach coupled with a semantically rich filtering process. we systematically link llm applications to specific occupations and use cases using both social media and news platforms with high-performing data collection and cleaning pipeline. unlike prior studies based on proprietary or short-term data, realm continuously aggregates public content across diverse domains over an extended time span, enabling a precise and nuanced understanding of how different professional sectors are integrating llms into their workflows, offering a robust foundation for future research. ## dataset construction the construction of the realm follows a structured pipeline for collecting and processing highquality data, as illustrated in figure 1 . a dedicated dashboard https://realm-e7682.web.app/ is also built to facilitate data exploration and visualization. construction details are presented below. data collection data in realm is sourced from reddit (via academic torrents foot_0 ) and news articles (retrieved using newsapi foot_1 ) spanning from june 2020 (when gpt-3 was released) to december 2024. this process narrows down billions of datapoints to a curated dataset of 94,000 entries, containing 15,000 reddit posts and 79,000 news articles. the real-time, user-generated content from reddit provides diverse perspectives, while news-api offers professionally curated articles, ensuring diversity. the data collection process begins with keywordbased extraction to isolate llm-related content, using a comprehensive list of model names obtained from github 3 . then we perform essential data cleaning to ensure the quality of the collected content. this includes removing duplicates, unusable tweets, stop words, and non-textual elements, ensuring the data is well-prepared for further processing. we employ a fine-tuned roberta model to filter irrevelant data, ensuring relevance by identifying descriptions of llm use cases or potential applications. the details of the roberta finetuning process are provided in appendix a.2. this filtering process prioritizes recall over precision to include all pertinent data, achieving high recall scores of 0.94 for news articles and 0.95 for reddit posts after human validation. following the initial filtering, a four-stage annotation pipeline, detailed in section 3, is applied to further refine the dataset by enhancing precision. ## analysis of realm dataset in this section, we present preliminary analysis results derived from the realm dataset. as depicted in figures 2a and 2b , the adoption and application of llms experienced a significant surge from between 2022 and 2023, coinciding with the release of gpt-3.5 in november 2022. figure 2a illustrates the number of llm use cases categorized by application type within news and reddit sources, respectively. these figures reveal that content creation and content synthesis are the most prevalent llm use cases, including writing assistance, summarization, and code generation. additionally, applications related to decision-making, digital assistance, and process automation have experienced substantial growth. the decision-making category includes activities such as recommending business strategies, optimizing marketing campaigns, and suggesting financial investment strategies. process automation involves streamlining routine and repetitive tasks. the increasing interest in these use cases indicates that users are perceiving llms as intelligent and reliable collaborators, thereby building trust in their capabilities to help daily work. this trend signifies that llms are evolving beyond their traditional role as generative ai, actively supporting and enhancing industrial workflows. however, certain areas such as robotics automation, recommendation systems, and vehicular automation have not yet extensively integrated llm technologies. figure 2b illustrates the utilization of llms across various occupations. in news sources, discussion were initially dominated by professionals in computer science and mathematics. however, over time, people in business and management have assumed a more prominent role in these discussions. in contrast, as reddit has a more technically inclined user base, its discussion is consistently dominated by individuals from computerrelated fields. but there has been a growing interest in llms among professionals in art & media, product development and education. figures 2c and 2d demonstrate varying levels of applicability and adoption of llm technologies across different professional domains: individuals in art, business, computer science, and management are the most engaged with llms across both news and reddit sources. their primary activities include content creation, content synthesis, decision-making, digital assistance, and process automation. additionally, professionals in computer science, mathematics, physical sciences, and social sciences are notably involved in discoveryoriented tasks, highlighting the potential of llms to advance scientific research. in contrast, occupations in the legal and sales sectors, as well as roles centered on manual or operational tasks (food preparation, transportation, and farming) exhibit limited engagement with llms. debiasing to address potential biases arising from the uneven distribution of occupational groups, we conducted a debiasing analysis to normalize raw llm use case statistics. certain fields, such as arts or business, may dominate the dataset due to higher overall content volume, artificially inflating their apparent llm adoption. we addressed this by applying a normalization procedure. we randomly sampled 1,000 data points from both the news and reddit corpora and manually annotated each item with its associated occupational group. based on these annotations, we estimated the discussion distribution across occupations: for each group i, we computed its discussion proportion d i , defined as the fraction of sampled items belonging to group i. this captures how prevalent each group is in the overall corpus, independent of llm-specific content. next, we normalized the number of llm use cases associated with each occupational group by dividing by d i , resulting in what we refer to as the llm exposure rate: this metric reflects the relative concentration of llm-related content within each group, adjusted for how frequently the group appears in the dataset overall. by using this normalization, we aim to distinguish true differences in llm adoption from artifacts of dataset imbalance. after debiasing (figure 3 ), the exposure rates of previously dominant fields such as business, management, and computer science decrease markedly relative to their raw use case counts (figure 3b ). this indicates that their initial prominence was primarily driven by the sheer volume of content associated with these occupations, rather than particularly high llm adoption. in contrast, domains such as education, healthcare, and art and media retain relatively high exposure rates even after normalization, suggesting that llm-related use cases constitute a larger share of discourse in these fields. furthermore, the side-by-side comparison in figure 3c highlights notable differences between reddit and news data, where user-driven domains like art and media and education exhibit higher reddit exposure, while business-related fields are more prominent in news coverage.