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    <abstract>Automated Answer Grading (AAG) is a fundamental task in intelligent education, requiring accurate semantic understanding and reliable modeling of student deviations from reference answers. Despite recent progress, large language models (LLMs) remain insensitive to missing key concepts, exhibit unstable scoring scales, and lack structured scoring semantics in their representation space. To overcome these limitations, we propose a difference-aware AAG framework that integrates heuristic difference labeling with dual-contrastive learning. Semantic difference levels between student and reference answers are automatically inferred through similarity-based heuristics and injected into the model input as explicit prompts, enabling fine-grained perception of semantic deviations. In addition, an InfoNCE-based contrastive objective enforces representation consistency among samples with identical scores, while a hierarchical contrastive constraint guided by score gaps promotes structured separation across different scoring levels. Experiments on benchmark datasets, including SciEntsBank and Beetle, show that the proposed method consistently outperforms cross-entropy–based baselines in accuracy, weighted accuracy, and relevance metrics. Further analyses demonstrate improved robustness and generalization, even when applied to small-scale models. We have made all datasets and the corresponding code publiclyaccessible at: https://github.com/leibnizchen/DiffCL</abstract>
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%0 Conference Proceedings
%T DiffCL: Difference-Aware Contrastive Learning for Automatic Answer Grading with Multi-Level Semantic Modeling
%A Chen, Lei
%A Gao, BoYu
%A Liu, Zitao
%A Wan, Tingjie
%A Luo, Weiqi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F chen-etal-2026-diffcl
%X Automated Answer Grading (AAG) is a fundamental task in intelligent education, requiring accurate semantic understanding and reliable modeling of student deviations from reference answers. Despite recent progress, large language models (LLMs) remain insensitive to missing key concepts, exhibit unstable scoring scales, and lack structured scoring semantics in their representation space. To overcome these limitations, we propose a difference-aware AAG framework that integrates heuristic difference labeling with dual-contrastive learning. Semantic difference levels between student and reference answers are automatically inferred through similarity-based heuristics and injected into the model input as explicit prompts, enabling fine-grained perception of semantic deviations. In addition, an InfoNCE-based contrastive objective enforces representation consistency among samples with identical scores, while a hierarchical contrastive constraint guided by score gaps promotes structured separation across different scoring levels. Experiments on benchmark datasets, including SciEntsBank and Beetle, show that the proposed method consistently outperforms cross-entropy–based baselines in accuracy, weighted accuracy, and relevance metrics. Further analyses demonstrate improved robustness and generalization, even when applied to small-scale models. We have made all datasets and the corresponding code publiclyaccessible at: https://github.com/leibnizchen/DiffCL
%R 10.18653/v1/2026.findings-acl.979
%U https://aclanthology.org/2026.findings-acl.979/
%U https://doi.org/10.18653/v1/2026.findings-acl.979
%P 19576-19589

Markdown (Informal)

[DiffCL: Difference-Aware Contrastive Learning for Automatic Answer Grading with Multi-Level Semantic Modeling](https://aclanthology.org/2026.findings-acl.979/) (Chen et al., Findings 2026)

• DiffCL: Difference-Aware Contrastive Learning for Automatic Answer Grading with Multi-Level Semantic Modeling (Chen et al., Findings 2026)

ACL

• Lei Chen, BoYu Gao, Zitao Liu, Tingjie Wan, and Weiqi Luo. 2026. DiffCL: Difference-Aware Contrastive Learning for Automatic Answer Grading with Multi-Level Semantic Modeling. In Findings of the Association for Computational Linguistics: ACL 2026, pages 19576–19589, San Diego, California, United States. Association for Computational Linguistics.