<ul data-eligibleForWebStory="true"><li>Feature transformation is crucial for enhancing data representation by creating new features from the original data.</li><li>Generative AI shows promise in this area but struggles with stable and error-free output generation.</li><li>Existing methods have limitations in ensuring both valid syntax and stable performance.</li><li>A new framework is proposed that combines LLMs' symbolic generation with ML's gradient optimization.</li><li>The proposed framework includes steps such as generating high-quality samples, embedding and searching for better feature transformations, distilling knowledge between LLMs, and combining ML and LLM probabilities for stable generation.</li><li>Experiments on various datasets show that this framework can improve downstream performance by 5% and reduce error cases by nearly half.</li><li>The results highlight the effectiveness and robustness of the collaborative approach.</li><li>The study also unveils interesting insights into LLMs' ability to understand original data.</li></ul>

LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation

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