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Recent studies have shown that large language models (LLMs), when customized with post-training on tabular data, can acquire general tabular in-context learning (TabICL) capabilities.
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LightGBM: A highly efficient gradient boosting decision tree
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Huang, X., Khetan, A., Cvitkovic, M., and Karnin, Z · 2020
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VIME: Extending the success of self- and semi-supervised learning to tabular domain
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TabNet: Attentive interpretable tabular learning
Arik, S. Ö. and Pfister, T · 2021
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Revisiting deep learning models for tabular data
Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A · 2021
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Net-DNF: Effective deep modeling of tabular data
Katzir, L., Elidan, G., and El-Yaniv, R · 2021
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What makes good in-context examples for gpt- 3 3 ?
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., and Chen, W · 2021
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Transformers can do bayesian inference
Müller, S., Hollmann, N., Arango, S. P., Grabocka, J., and Hutter, F · 2021
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SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training
Pre-training to learn in context
Gu, Y., Dong, L., Wei, F., and Huang, M · 2023
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TabLLM: Few-shot classification of tabular data with large language models
Hegselmann, S., Buendia, A., Lang, H., Agrawal, M., Jiang, X., and Sontag, D · 2023
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TabPFN: A transformer that solves small tabular classification problems in a second
Hollmann, N., Müller, S., Eggensperger, K., and Hutter, F · 2023
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Transfer learning with deep tabular models
Levin, R., Cherepanova, V., Schwarzschild, A., Bansal, A., Bruss, C. B., Goldstein, T., Wilson, A. G., and Goldblum, M · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G · 2023
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Somepalli, G., Goldblum, M., Schwarzschild, A., Bruss, C. B., and Goldstein, T · 2021
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SubTab: Subsetting features of tabular data for self-supervised representation learning
Ucar, T., Hajiramezanali, E., and Edwards, L · 2021
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SCARF: Self-supervised contrastive learning using random feature corruption
Bahri, D., Jiang, H., Tay, Y., and Metzler, D · 2022
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LIFT: Language-interfaced fine-tuning for non-language machine learning tasks
Dinh, T., Zeng, Y., Zhang, R., Lin, Z., Gira, M., Rajput, S., Sohn, J.-y., Papailiopoulos, D., and Lee, K · 2022
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A survey on in-context learning
Dong, Q., Li, L., Dai, D., Zheng, C., Wu, Z., Chang, B., Sun, X., Xu, J., and Sui, Z · 2022
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Why do tree-based models still outperform deep learning on typical tabular data?
Grinsztajn, L., Oyallon, E., and Varoquaux, G · 2022
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Tabular data: Deep learning is not all you need
Shwartz-Ziv, R. and Armon, A · 2022
Cited alongside, same era.
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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Effective long-context scaling of foundation models
Xiong, W., Liu, J., Molybog, I., Zhang, H., Bhargava, P., Hou, R., Martin, L., Rungta, R., Sankararaman, K. A., Oguz, B., et al · 2023
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$k$NN prompting: Beyond-context learning with calibration-free nearest neighbor inference
Xu, B., Wang, Q., Mao, Z., Lyu, Y., She, Q., and Zhang, Y · 2023
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XTab: Cross-table pretraining for tabular transformers
Zhu, B., Shi, X., Erickson, N., Li, M., Karypis, G., and Shoaran, M · 2023
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Phi-3 technical report: A highly capable language model locally on your phone
Abdin, M., Jacobs, S. A., Awan, A. A., Aneja, J., Awadallah, A., Awadalla, H., Bach, N., Bahree, A., Bakhtiari, A., Behl, H., et al · 2024
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Large scale transfer learning for tabular data via language modeling
Gardner, J. P., Perdomo, J. C., and Schmidt, L · 2024
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TabR: Tabular deep learning meets nearest neighbors
Gorishniy, Y., Rubachev, I., Kartashev, N., Shlenskii, D., Kotelnikov, A., and Babenko, A · 2024
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LongICLBench: Long-context llms struggle with long in-context learning
Li, T., Zhang, G., Do, Q. D., Yue, X., and Chen, W · 2024
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From supervised to generative: A novel paradigm for tabular deep learning with large language models
Wen, X., Zhang, H., Zheng, S., Xu, W., and Bian, J · 2024
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Making pre-trained language models great on tabular prediction
Yan, J., Zheng, B., Xu, H., Zhu, Y., Chen, D., Sun, J., Wu, J., and Chen, J · 2024
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UniTabE: A universal pretraining protocol for tabular foundation model in data science
Yang, Y., Wang, Y., Liu, G., Wu, L., and Liu, Q · 2024
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Accurate predictions on small data with a tabular foundation model
Hollmann, N., Müller, S., Purucker, L., Krishnakumar, A., Körfer, M., Hoo, S. B., Schirrmeister, R. T., and Hutter, F · 2025
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