LLMs on Tabular Data with Limited Semantics: Evidence from Industrial Car Retrofit Prediction
arXiv:2606.15314v1 Announce Type: cross Abstract: Industrial retrofit planning depends on structured operational data rather than free text: planners must estimate whether a newly registered prototype will require a retrofit, which retrofit package it will need, and how long the work will take. We study an industrial dataset linking a prototype-registration system (284,271 vehicles) with a retrofit-management system (48,716 cleaned visits), and compare strong tabular machine learning baselines with three LLM-based strategies on row-serialized inputs: embedding features (Amazon Titan), direct p
The proliferation of increasingly capable Large Language Models (LLMs) and the demand for their application beyond traditional text-based tasks drives this exploration into structured data.
This research provides early evidence and methodology for deploying LLMs on critical industrial tabular data, potentially expanding their utility into core operational workflows previously dominated by traditional machine learning.
The perceived limitations of LLMs primarily to text are being challenged, suggesting they can extract value from structured enterprise data, offering new approaches to predictive analytics in industrial settings.
- · AI/ML researchers
- · Enterprises with complex tabular data
- · LLM providers
- · Traditional tabular ML solutions (certain use cases)
LLMs demonstrate a promising new architecture for interpreting and acting on structured, non-semantic industrial datasets.
Increased adoption of LLM-based solutions for predictive maintenance, supply chain optimization, and operational planning in manufacturing and automotive sectors.
The development of specialized 'tabular-first' LLMs or hybrid models that seamlessly integrate symbolic reasoning with neural capabilities for enterprise data.
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Read at arXiv cs.AI