arXiv:2606.10327v1 Announce Type: new Abstract: Automated Essay Scoring (AES) systems must judge interdependent discourse elements (e.g., lead, claim, evidence, conclusion), yet most approaches treat these in isolation, harming coherence and generalization. We investigate task-aware fine-tuning of LLaMA-3.1-8B for AES using parameter-efficient LoRA with 4-bit quantization and compare three training curricula: (i) Sequential (progressively fine-tuning on lead, then position, then claim, then evidence, then conclusion), (ii) Independent (task-specific models), and (iii) Randomized (shuffled mult
The rapid advancement and accessibility of large language models like LLaMA-3.1-8B are enabling researchers to explore more sophisticated fine-tuning techniques for specialized AI tasks.
This research contributes to improving the coherence and generalization of AI in complex cognitive tasks like Automated Essay Scoring, which has significant implications for education, content generation, and AI's ability to handle structured textual analysis.
The understanding of how sequential fine-tuning curricula can significantly enhance AI performance in tasks requiring interdependent discourse element analysis, potentially leading to more robust and accurate AI agents.
- · AI researchers
- · Educational technology sector
- · LLM developers
- · Students
- · Traditional essay scoring methods
- · AI models lacking sophisticated fine-tuning
Automated Essay Scoring systems become more accurate and generalized, reducing human workload.
Improved AES leads to more personalized and immediate feedback for students, enhancing learning outcomes.
The methodology for sequential fine-tuning could be generalized to other complex hierarchical tasks, accelerating AI agent development beyond text analysis.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL