Data transformation is a critical challenge in modern data management systems, particularly when handling complex operations over multiple data sources. However, existing approaches rely on supervised learning, which requires tremendous data labeling and training overhead. To alleviate such overhead while improving accuracy, we demonstrate a novel system DataMorpher that leverages Large Language Models (LLMs) to generate code that transforms source datasets into a user-specified target format. To generate a high-quality and token-efficient prompt, we leverage data profiling to extract features from the source datasets and historical examples of the target data. We also select a subset of features to reduce noise and costs using a ranking algorithm. These selected features are finally translated into a declarative language, which is inspired by SQL's data definition language (DDL), before being added to the prompt. We will demonstrate the workflow and effectiveness of DATAMORPHER using real-world data transformation workflows from Microsoft's GitHub benchmark, smart building, and medical data integration. (A5-min video of our demo is available at https://youtu.be/CuDm46K-_eA.)

OBJECTIVE: Data extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) that mimics the real-world 2-reviewer process. MATERIALS AND METHODS: A dataset of 10 trials (22 publications) from a published LSR was used, focusing on 23 variables related to trial, population, and outcomes data. The dataset was split into prompt development (n = 5) and held-out test sets (n = 17). GPT-4-turbo and Claude-3-Opus were used for data extraction. Responses from the 2 LLMs were considered concordant if they were the same for a given variable. The discordant responses from each LLM were provided to the other LLM for cross-critique. Accuracy, ie, the total number of correct responses divided by the total number of responses, was computed to assess performance. RESULTS: In the prompt development set, 110 (96%) responses were concordant, achieving an accuracy of 0.99 against the gold standard. In the test set, 342 (87%) responses were concordant. The accuracy of the concordant responses was 0.94. The accuracy of the discordant responses was 0.41 for GPT-4-turbo and 0.50 for Claude-3-Opus. Of the 49 discordant responses, 25 (51%) became concordant after cross-critique, increasing accuracy to 0.76. DISCUSSION: Concordant responses by the LLMs are likely to be accurate. In instances of discordant responses, cross-critique can further increase the accuracy. CONCLUSION: Large language models, when simulated in a collaborative, 2-reviewer workflow, can extract data with reasonable performance, enabling truly "living" systematic reviews.