Abstract
Tabular learning uses structured data to predict target outcomes. Traditionally, this process has relied on labeled data. However, large language models (LLMs) can be used to elicit domain priors based on the task description and feature semantics, thereby enabling predictions without labeled data. We propose Marginal Response Surface Elicitation (MARS), a method that transforms feature-level LLM priors into a reusable, zero-shot tabular classifier. To construct this classifier, MARS selects representative values for each feature from unlabeled data and prompts the LLM to provide corresponding class support scores and feature weights. It then aggregates multiple responses using the median to construct feature response functions, and makes predictions through their weighted sum without further LLM queries. Across eight tabular benchmark tasks, MARS achieves the highest average AUC and AP, outperforming direct prompting by 1.97 and 6.21 percentage points respectively, while substantially reducing end-to-end costs. Evaluations with LLMs of different sizes further demonstrate its predictive advantage over direct prompting.
Keywords
Subject
Publication details
- Journal
- Not available
- Open access
- Green open access
Cite this article
APA 7
Teng, L., Ding, Y., Liu, J., Liu, H., Guo, J., Li, H., Zhang, J., & Song, L. (2026). Marginal Response Surface Elicitation for Zero-Label Tabular Learning. https://omanscience.com/en/articles/marginal-response-surface-elicitation-for-zero-label-tabular-learning
MLA 9
Teng, Liangyu, et al. "Marginal Response Surface Elicitation for Zero-Label Tabular Learning." https://omanscience.com/en/articles/marginal-response-surface-elicitation-for-zero-label-tabular-learning.
Chicago (author–date)
Teng, Liangyu, Yicheng Ding, Jing Liu, Hengsong Liu, Juncen Guo, Hongru Li, Jingyu Zhang, and Liang Song. 2026. "Marginal Response Surface Elicitation for Zero-Label Tabular Learning." https://omanscience.com/en/articles/marginal-response-surface-elicitation-for-zero-label-tabular-learning.
Harvard
Teng, L., Ding, Y., Liu, J., Liu, H., Guo, J., Li, H., Zhang, J. and Song, L. (2026) 'Marginal Response Surface Elicitation for Zero-Label Tabular Learning', Available at: https://omanscience.com/en/articles/marginal-response-surface-elicitation-for-zero-label-tabular-learning.
Vancouver
Teng L, Ding Y, Liu J, Liu H, Guo J, Li H, et al. Marginal Response Surface Elicitation for Zero-Label Tabular Learning. https://omanscience.com/en/articles/marginal-response-surface-elicitation-for-zero-label-tabular-learning
IEEE
L. Teng, Y. Ding, J. Liu, H. Liu, J. Guo, H. Li, J. Zhang, and L. Song, "Marginal Response Surface Elicitation for Zero-Label Tabular Learning," https://omanscience.com/en/articles/marginal-response-surface-elicitation-for-zero-label-tabular-learning.