用分层注意力模型提升高校招生决策的准确与公平性
BGM-HAN: A Hierarchical Attention Network for Accurate and Fair Decision Assessment on Semi-Structured Profiles
- 构建分层注意力网络，融合字节编码与门控机制建模申请材料
- 在真实招生数据上超越传统模型与大语言模型表现
- 适合需要公平性与可解释性的高风险决策场景使用
高风险领域的人类决策常依赖专业经验与启发式判断，但易受难以察觉的认知偏见影响，威胁公平性与长期效果。本文以高校招生为例，提出BGM-HAN——一种增强型字节对编码、门控多头分层注意力网络，用于有效建模半结构化申请人数据。该模型捕捉多层次语义表示，提升评估的精细度、可解释性与预测性能。在真实招生数据上的实验表明，BGM-HAN显著优于从传统机器学习到大语言模型的多种先进基线方法，为结构、上下文与公平性并重的决策领域提供了一种有前景的辅助框架。源代码见：https://github.com/junhua/bgm-han。
原文摘要 · Abstract (English)
Human decision-making in high-stakes domains often relies on expertise and heuristics, but is vulnerable to hard-to-detect cognitive biases that threaten fairness and long-term outcomes. This work presents a novel approach to enhancing complex decision-making workflows through the integration of hierarchical learning alongside various enhancements. Focusing on university admissions as a representative high-stakes domain, we propose BGM-HAN, an enhanced Byte-Pair Encoded, Gated Multi-head Hierarchical Attention Network, designed to effectively model semi-structured applicant data. BGM-HAN captures multi-level representations that are crucial for nuanced assessment, improving both interpretability and predictive performance. Experimental results on real admissions data demonstrate that our proposed model significantly outperforms both state-of-the-art baselines from traditional machine learning to large language models, offering a promising framework for augmenting decision-making in domains where structure, context, and fairness matter. Source code is available at: https://github.com/junhua/bgm-han.
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