不依赖数据源，通过合并模型参数实现更优的作文评分。
Composable Cross-prompt Essay Scoring by Merging Models
- 用任务向量线性组合各源模型参数，模拟联合训练。
- 在跨数据集场景下评分准确率超越联合训练方法。
- 适合需隐私保护与高效适应的自动评分系统。
近期跨提示自动作文评分（AES）多采用联合训练所有源提示的模型，常需额外访问目标提示的未标注作文，且重用源数据集引发隐私问题。本文提出无源适配方法：不使用数据，仅选择性合并独立训练的源模型参数。通过线性组合微调产生的任务向量模拟联合训练，并设计先验编码的信息最大化（PIM）无监督目标，利用源数据预计算的先验增强评分区分度。采用贝叶斯优化高效求解系数。在大语言模型上进行的实验表明，该方法在同数据集与跨数据集适配中均优于联合训练，在严重分布偏移下表现更优，且保持计算高效。
原文摘要 · Abstract (English)
Recent advances in cross-prompt automated essay scoring (AES) typically train models jointly on all source prompts, often requiring additional access to unlabeled target prompt essays simultaneously. However, using all sources is suboptimal in our pilot study, and re-accessing source datasets during adaptation raises privacy concerns. We propose a source-free adaptation approach that selectively merges individually trained source models' parameters instead of datasets. In particular, we simulate joint training through linear combinations of task vectors -- the parameter updates from fine-tuning. To optimize the combination's coefficients, we propose Prior-encoded Information Maximization (PIM), an unsupervised objective which promotes the model's score discriminability regularized by priors pre-computed from the sources. We employ Bayesian optimization as an efficient optimizer of PIM. Experimental results with LLMs on in-dataset and cross-dataset adaptation show that our method (1) consistently outperforms training jointly on all sources, (2) maintains superior robustness compared to other merging methods, (3) excels under severe distribution shifts where recent leading cross-prompt methods struggle, all while retaining computational efficiency.
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