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researcher

Yang Zhao

10 papers hereh-index 470 citations15 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author4

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CL5
  • cs.LG3
  • cs.AI2
same name
  • Yang Zhao — 35 papers, h 32
  • Yang Zhao — 35 papers, h 14
  • Yang Zhao — 32 papers, h 50
  • Yang Zhao — 30 papers
  • Yang Zhao — 18 papers, h 20
  • Yang Zhao — 17 papers, h 14

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

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

1 citations · 1 across the 8 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.