用轻量残差修正提升长文本评论排序精度
RLPO: Residual Listwise Preference Optimization for Long-Context Review Ranking
- 先用大模型打分再补全全局偏好残差
- 在100条以上长列表中仍保持高NDCG
- 适合电商长评排序与大模型融合场景
评论排序在电商中至关重要，能从海量用户生成内容中筛选出有诊断价值的真实反馈。尽管大语言模型提升了语义评估能力，现有排序范式在长上下文场景下仍面临效率与效果的权衡：点对点评分高效但忽略列表级交互，导致前k名排序失准；列表级方法虽可利用全局信息，却计算开销大且随候选列表增长而不稳定。为此，我们提出残差列表偏好优化（RLPO），将排序建模为强点对点大模型评分器上的表示级残差修正。RLPO先生成校准后的点对点分数与项表示，再通过轻量编码器对表示预测列表级分数残差，避免全标记级列表处理。我们还构建了一个大规模、经人工验证的长上下文评论排序基准。实验表明，RLPO在多种基线之上提升NDCG@k表现，且在列表长度增加时依然稳健。
原文摘要 · Abstract (English)
Review ranking is pivotal in e-commerce for prioritizing diagnostic and authentic feedback from the deluge of user-generated content. While large language models have improved semantic assessment, existing ranking paradigms face a persistent trade-off in long-context settings. Pointwise scoring is efficient but often fails to account for list-level interactions, leading to miscalibrated top-$k$ rankings. Listwise approaches can leverage global context, yet they are computationally expensive and become unstable as candidate lists grow. To address this, we propose Residual Listwise Preference Optimization (RLPO), which formulates ranking as listwise representation-level residual correction over a strong pointwise LLM scorer. RLPO first produces calibrated pointwise scores and item representations, then applies a lightweight encoder over the representations to predict listwise score residuals, avoiding full token-level listwise processing. We also introduce a large-scale benchmark for long-context review ranking with human verification. Experiments show RLPO improves NDCG@k over strong pointwise and listwise baselines and remains robust as list length increases.
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