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LLMs on Tabular Data with Limited Semantics: Evidence from Industrial Car Retrofit Prediction

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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.

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Authors: Aina Vila Pons, Ioannis Tzachristas, Constantinos Antoniou
Released: 2026-06-16 (23d ago)
Topic: Related to current project
Relation to my work: Read next because LLMs on Tabular Data with Limited Semantics: Evidence from Industrial Car Retrofit Prediction overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, text, class, rect, line, rate, compare, alone. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.15314

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