[论文] STAR-OPD: Structured Aspect-Cascade-Aware On-Policy Reward Distillation for ABSA Quadruple Extraction
来源：arXiv AI 论文收录：2026/8/24
✦ AI 解读
本文针对基于方面的情感分析（ABSA）四元组抽取任务，提出STAR-OPD方法，通过结构化方面级联感知的在策略奖励蒸馏，解决大模型蒸馏至小模型时出现的结构无效状态（如目标-方面绑定断裂、幻觉目标）问题。该方法优于传统离策略蒸馏，能有效提升小模型在细粒度情感抽取上的性能。
Aspect-based sentiment analysis (ABSA) quadruple extraction requires jointly predicting target, aspect, opinion, and sentiment over reviews that often contain multiple fine-grained sentiment tuples. While large chain-of-thought (CoT) models perform well on this task, distilling them into smaller deployable models remains difficult. We identify a task-specific failure mode in distilled ABSA extraction: student errors at the target-aspect interface create structurally invalid states, such as broken target-aspect bindings and hallucinated targets, which then corrupt downstream predictions. Conventional off-policy distillation is poorly suited to this setting because it trains only on teacher-generated trajectories and provides little supervision on the student-induced structural states that d
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