Institution profile

China University of Political Science and Law

Academic institutionasia · cn
Official website
Research library3linked papers
Opportunities0open roles
Selected work

Representative Papers

Logics of Filter Bubbles

Sep 18, 2026

研究针对过滤气泡问题,通过开发静态和动态逻辑系统来推理过滤气泡的形成与维持机制。

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Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM

Feb 16, 2025

To address the ranking performance bottleneck in similar-case retrieval for legal AI, this paper proposes a novel learning-to-rank paradigm that bypasses the final classification layer of language models—thereby mitigating overfitting induced by class imbalance in fine-tuning. Specifically, RankSVM is introduced into the Chinese legal domain to replace the standard fully connected layer in traditional fine-tuning pipelines. We present the first systematic pairwise ranking framework integrating BERT/ERNIE with RankSVM, and rigorously evaluate it on the LeCaRDv1 and LeCaRDv2 benchmarks. Experimental results demonstrate consistent and statistically significant improvements: average gains of 2.3%–4.1% in both NDCG@5 and MAP, alongside enhanced model generalization. The implementation is publicly available.

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Recent publications

Latest Papers

Logics of Filter Bubbles

Sep 18, 2026

研究针对过滤气泡问题,通过开发静态和动态逻辑系统来推理过滤气泡的形成与维持机制。

0 citationsRead paper

Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM

Feb 16, 2025

To address the ranking performance bottleneck in similar-case retrieval for legal AI, this paper proposes a novel learning-to-rank paradigm that bypasses the final classification layer of language models—thereby mitigating overfitting induced by class imbalance in fine-tuning. Specifically, RankSVM is introduced into the Chinese legal domain to replace the standard fully connected layer in traditional fine-tuning pipelines. We present the first systematic pairwise ranking framework integrating BERT/ERNIE with RankSVM, and rigorously evaluate it on the LeCaRDv1 and LeCaRDv2 benchmarks. Experimental results demonstrate consistent and statistically significant improvements: average gains of 2.3%–4.1% in both NDCG@5 and MAP, alongside enhanced model generalization. The implementation is publicly available.

0 citationsRead paper