Rules-Injected Instruction Tuning (RuleGPT), a paradigm that injects IF–THEN rules extracted from training data directly into the LLM's instruction sequence, allows the LLM to focus on higher-order, context-dependent interactions.
Wei-Hao Jiang, Huizhao Wang, Zhihui Hu et al.· Proceedings of the 32nd ACM...· 0 citations
CoFE (Collaborative Feature Engineering) is proposed, a two-phase framework that tightly couples search-based exploration with LLM-driven reasoning and consistently outperforms state-of-the-art data-driven and LLM-based AutoFE methods on the majority of datasets, while offering favorable computational efficiency.
Weihao Jiang, Ziang Nan, Zhihui Shi et al.· Proceedings of the 32nd ACM...· 0 citations
In domains such as finance, healthcare, and industry, feature engineering remains the key bottleneck limiting the performance of machine learning models on tabular data. While Automated Feature Engineering (AutoFE) aims to reduce this manual effort, existing approaches still suffer from distinct limitations: data-drive...
Weihao Jiang, Ziang Nan, Zhihui Shi et al.· Proceedings of the 32nd ACM...· 0 citations
Rules-Injected Instruction Tuning (RuleGPT), a paradigm that injects IF–THEN rules extracted from training data directly into the LLM's instruction sequence, allows the LLM to focus on higher-order, context-dependent interactions.
Weihao Jiang, Huizhao Wang, Zhihui Hu et al.· Proceedings of the 32nd ACM...· 0 citations
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