LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)
arXiv:2606.09004v1 Announce Type: new Abstract: Feature engineering remains essential for tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for automating this process, giving rise to LLM-powered AuTomated Tabular feature Engineering (LATTE). However, the absence of standardized platforms prevents fair, cost-aware comparisons. Furthermore, complex methodological designs obscure the specific contributions of individual components; for example, although LFG integrates Tree-of-Thought, few-shot demonstrations, Monte Carlo Tree Search, and natural languag
The rapid advancement of Large Language Models has made their application to complex tasks like feature engineering an active area of research, pushing for standardized evaluation methods.
Standardized evaluation frameworks are crucial for comparing and accelerating the development of LLM-powered tools, which can significantly enhance data analysis and AI model performance.
The introduction of LATTEArena could establish a benchmark for feature engineering, allowing for more objective assessment and faster iteration in the development of AI agents.
- · AI researchers
- · Data scientists
- · LLM developers
- · Companies with large tabular datasets
- · Manual feature engineering consultancies (eventually)
- · Less efficient LLM-powered feature engineering solutions
The adoption of LATTEArena will streamline the comparison and improvement of LLM-based feature engineering techniques.
Improved feature engineering efficiency will lead to more accurate and faster development of AI models across various industries.
The automation of this critical data science step could further embed AI agents into analytical workflows, fundamentally changing demand for data scientists.
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Read at arXiv cs.AI