Long Text to Predictive Features: LLM-Guided Blockwise Feature Engineering via Executable Program Search
Industrial risk-control systems typically rely on structured-data models for efficient prediction, yet substantial valuable information remains embedded in unstructured long text. Extracting this information through manual feature engineering is labor-intensive, while requiring a large language model (LLM) to process every real-time input may not meet practical deployment requirements. To address this challenge, we propose LLM-BlockFE, an LLM-guided offline feature construction framework that converts long text into executable feature programs, thereby avoiding LLM calls during online inference. Authors: Ziming Dai, Dabiao Ma, Ziheng Guo.
Why it matters
Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Industrial risk-control systems typically rely on structured-data models for efficient prediction, yet substantial valuable information remains embedded in unstructured long text.
