用共享模型+轻量适配器实现27类作业自动评分，提速40%还省60%显存。
Efficient Multi-Task Inferencing with a Shared Backbone and Lightweight Task-Specific Adapters for Automatic Scoring
- 共享主干+轻量LoRA适配器，支持27种任务的高效微调。
- 平均QWK达0.848，仅比全量微调低0.04，但显存减少60%。
- 适合教育领域需低成本部署的自动化评分场景。
人工智能在教育中的应用需要可扩展且高效的框架，在性能、适应性和成本间取得平衡。本文提出一种共享主干模型架构，结合轻量级LoRA适配器，用于27个互斥任务的自动化学生作答评分。该框架在保持竞争性表现（平均QWK为0.848，相比全量微调模型的0.888仅低0.04）的同时，将GPU内存消耗降低60%，推理延迟减少40%，展现出显著的效率提升。该方法契合研讨会对改进语言模型以服务教育任务的关注，推动了成本敏感场景下的负责任创新，并通过简化评估流程支持教育者工作。研究结果凸显了可扩展AI在提升学习效果的同时，维持自动化评分系统公平性与透明性的潜力。
原文摘要 · Abstract (English)
The integration of Artificial Intelligence (AI) in education requires scalable and efficient frameworks that balance performance, adaptability, and cost. This paper addresses these needs by proposing a shared backbone model architecture enhanced with lightweight LoRA adapters for task-specific fine-tuning, targeting the automated scoring of student responses across 27 mutually exclusive tasks. By achieving competitive performance (average QWK of 0.848 compared to 0.888 for fully fine-tuned models) while reducing GPU memory consumption by 60% and inference latency by 40%, the framework demonstrates significant efficiency gains. This approach aligns with the workshop's focus on improving language models for educational tasks, creating responsible innovations for cost-sensitive deployment, and supporting educators by streamlining assessment workflows. The findings underscore the potential of scalable AI to enhance learning outcomes while maintaining fairness and transparency in automated scoring systems.
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