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20212026
most citedOpenFE: Automated Feature Generation with Expert-level Performance

8 citations · 16 across the 15 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

Consolidating Rewarded Perturbations for LLM Post-Training

Zheyu Zhang, Shuo Yang, Gjergji Kasneci

Post-training of language models is commonly framed as a sample-score-update loop implemented by gradient descent. A recent line of work, exemplified by RandOpt, relocates this loo…

cs.CL2026

Moral Lenses, Political Coordinates: Towards Ideological Positioning of Morally Conditioned LLMs

Chenchen Yuan, Bolei Ma, Zheyu Zhang +3

While recent research has systematically documented political orientation in large language models (LLMs), existing evaluations rely primarily on direct probing or demographic pers…

cs.CL2025

Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models

Zheyu Zhang, Shuo Yang, Bardh Prenkaj +1

Large Language Models (LLMs) have shown strong potential for tabular data generation by modeling textualized feature-value pairs. However, tabular data inherently exhibits sparse f…

cs.CL2025

Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models

Chenchen Yuan, Zheyu Zhang, Shuo Yang +2

Large Language Models (LLMs) have shown impressive moral reasoning abilities. Yet they often diverge when confronted with complex, multi-factor moral dilemmas. To address these dis…

cs.CL2023

Autonomous Tree-search Ability of Large Language Models

Zheyu Zhang, Zhuorui Ye, Yikang Shen +1

Large Language Models have excelled in remarkable reasoning capabilities with advanced prompting techniques, but they fall short on tasks that require exploration, strategic foresi…

cs.CL2023★ 2 cited

ModuleFormer: Modularity Emerges from Mixture-of-Experts

Yikang Shen, Zheyu Zhang, Tianyou Cao +3

Large Language Models (LLMs) have achieved remarkable results. However, existing models are expensive to train and deploy, and it is also difficult to expand their knowledge beyond…