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20232026
most citedThe Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit

1 citations · 3 across the 10 of their papers we have counts for

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cs.LG2025★ 1 cited

Harnessing LLMs Explanations to Boost Surrogate Models in Tabular Data Classification

Ruxue Shi, Hengrui Gu, Xu Shen +1

Large Language Models (LLMs) have shown remarkable ability in solving complex tasks, making them a promising tool for enhancing tabular learning. However, existing LLM-based method…

cs.LG2025

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning

Ruxue Shi, Hengrui Gu, Hangting Ye +3

Few-shot tabular learning, in which machine learning models are trained with a limited amount of labeled data, provides a cost-effective approach to addressing real-world challenge…