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.

翻译：可持续发展报告对ESG评估至关重要，但绿色漂洗与模糊声明常损害其可信度。现有NLP模型对此类实践缺乏鲁棒性，通常依赖泛化能力薄弱的表层模式。我们提出一种参数高效的框架，通过结合对比学习与序数排序目标来结构化LLM潜在空间，以捕捉具体行动与模糊声明之间的梯度差异。该方法采用门控特征调制以过滤披露噪声，并利用MetaGradNorm稳定多目标优化。跨类别实验表明，相较于标准基线方法，本框架具有更优的鲁棒性，同时揭示了表征刚性与泛化能力之间的权衡关系。