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20242026
most citedCausal-Guided Active Learning for Debiasing Large Language Models

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

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cs.CL2025

Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning

Yang He, Xiao Ding, Bibo Cai +5

While reasoning-augmented large language models (RLLMs) significantly enhance complex task performance through extended reasoning chains, they inevitably introduce substantial unne…

cs.CL2025

Information Gain-Guided Causal Intervention for Autonomous Debiasing Large Language Models

Zhouhao Sun, Xiao Ding, Li Du +5

Despite significant progress, recent studies indicate that current large language models (LLMs) may still capture dataset biases and utilize them during inference, leading to the p…

cs.LG2025

UFO-RL: Uncertainty-Focused Optimization for Efficient Reinforcement Learning Data Selection

Yang Zhao, Kai Xiong, Xiao Ding +9

Scaling RL for LLMs is computationally expensive, largely due to multi-sampling for policy optimization and evaluation, making efficient data selection crucial. Inspired by the Zon…

cs.CL2025

Large Language Models Are Still Misled by Simple Bias Ensembles

Zhouhao Sun, Zhiyuan Kan, Xiao Ding +5

With the evolution of large language models (LLMs), their robustness against individual simple biases has been enhanced. However, we observe that the ensemble of multiple simple bi…

cs.CL2025

Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection

Yang Zhao, Li Du, Xiao Ding +10

Large language models (LLMs) have shown great potential across various industries due to their remarkable ability to generalize through instruction tuning. However, the limited ava…