Papers by Peidong Guo
MaskTab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification (2026.findings-acl)
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| Challenge: | Tabular data is high-dimensional, riddled with missing entries, and rarely labeled at scale. |
| Approach: | They propose a unified pre-training framework for industrial-scale tabular data . MaskTab encodes missing values via dedicated learnable tokens . |
| Outcome: | The proposed framework outperforms XGBoost and MaskTab-L on industrial-scale . it achieves +5.04% AUC and +8.28% KS over prior art under rigorous scaling . |
Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs (2024.acl-long)
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| Challenge: | Existing methods to determine the knowledge an LLM already possesses and the knowledge that requires the help of a search engine are expensive and require excessive computational costs. |
| Approach: | They propose a slim proxy model that detects missing knowledge in LLMs with a proxy model and use it to perform retrieval for the missing knowledge. |
| Outcome: | The proposed approach detects missing knowledge in LLMs with a slim proxy model and takes its answers as heuristic answers. |