AI Peers Exert Social Influence on Human Dishonesty in Groups
研究通过两阶段随机行为实验探讨了AI同伴对群体中人类不诚实行为的影响，发现AI与人类同伴具有相似的社会影响力。
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Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM
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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AI Peers Exert Social Influence on Human Dishonesty in Groups
研究通过两阶段随机行为实验探讨了AI同伴对群体中人类不诚实行为的影响，发现AI与人类同伴具有相似的社会影响力。
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Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM
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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