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Zhiming Lin

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AAAI Conference 2026 Conference Paper

CEC-Zero: Zero-Supervision Character Error Correction with Self-Generated Rewards

  • Zhiming Lin
  • Kai Zhao
  • Sophie Zhang
  • Peilai Yu
  • Canran Xiao

Large-scale Chinese spelling correction (CSC) remains critical for real-world text processing, yet existing LLMs and supervised methods lack robustness to novel errors and rely on costly annotations. We introduce CEC-Zero, a zerosupervision reinforcement learning framework that addresses this by enabling LLMs to correct their own mistakes. CEC-Zero synthesizes errorful inputs from clean text, computes cluster-consensus rewards via semantic similarity and candidate agreement, and optimizes the policy with PPO. It outperforms supervised baselines by 10–13 F1 points and strong LLM fine-tunes by 5–8 points across 9 benchmarks, with theoretical guarantees of unbiased rewards and convergence.CEC-Zero establishes a label-free paradigm for robust, scalable CSC, unlocking LLM potential in noisy text pipelines.

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AAAI Conference 2026 Conference Paper

From Points to Coalitions: Hierarchical Contrastive Shapley Values for Prioritizing Data Samples

  • Canran Xiao
  • Jiabao Dou
  • Zhiming Lin
  • Zong Ke
  • Liwei Hou

How should we quantify the value of each training example when datasets are large, heterogeneous, and geometrically structured? Classical Data-Shapley answers in principle, but its O(n!) complexity and point-wise perspective are ill-suited to modern scales. We propose Hierarchical Contrastive Data Valuation (HCDV), a three-stage framework that (i) learns a contrastive, geometry-preserving representation, (ii) organizes the data into a balanced coarse-to-fine hierarchy of clusters, and (iii) assigns Shapley-style pay-offs to coalitions via local Monte-Carlo games whose budgets are propagated downward. HCDV collapses the factorial burden to O(T∑ℓKℓ) = O(TKmax log n), rewards examples that sharpen decision boundaries, and regularizes outliers through curvature-based smoothness. We prove that HCDV approximately satisfies the four Shapley axioms with surplus loss O(η log n), enjoys sub-Gaussian coalition deviation Õ(1/√T), and incurs at most kε∞ regret for top-k selection. Experiments on four benchmarks — tabular, vision, streaming, and a 45 M-sample CTR task — plus the OpenDataVal suite show that HCDV lifts accuracy by up to +5 pp, slashes valuation time by up to 100×, and directly supports tasks such as augmentation filtering, low-latency streaming updates, and fair marketplace payouts.

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