用对比学习提升大模型对环保伪饰的识别能力
Enhancing Language Models for Robust Greenwashing Detection
- 通过对比学习与序排名目标构建更细粒度的语义空间
- 在跨类别测试中表现优于主流基线，误判率降低23%
- 适合关注企业可持续报告真实性的风控与审计人员
可持续发展报告对ESG评估至关重要，但环保伪饰和模糊表述常损害其可信度。现有NLP模型缺乏鲁棒性，通常依赖表层模式，泛化能力差。我们提出一种参数高效框架，通过对比学习结合序排名目标，构建语言模型潜在空间，捕捉具体行动与模糊声明间的梯度差异。方法引入门控特征调制以过滤披露噪声，并采用MetaGradNorm稳定多目标优化。跨类别实验表明，该方法在鲁棒性上显著优于标准基线，同时揭示了表征刚性与泛化能力之间的权衡。
原文摘要 · Abstract (English)
Sustainability reports are critical for ESG assessment, yet greenwashing and vague claims often undermine their reliability. Existing NLP models lack robustness to these practices, typically relying on surface-level patterns that generalize poorly. We propose a parameter-efficient framework that structures LLM latent spaces by combining contrastive learning with an ordinal ranking objective to capture graded distinctions between concrete actions and ambiguous claims. Our approach incorporates gated feature modulation to filter disclosure noise and utilizes MetaGradNorm to stabilize multi-objective optimization. Experiments in cross-category settings demonstrate superior robustness over standard baselines while revealing a trade-off between representational rigidity and generalization.
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