From Granular Revision Operations to Meaningful Revision Units: Evaluating LLMs for Revision Boundary Detection

arXiv:2609.33720 · cs.CL, cs.AI · Submitted 2026-09-27 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Trace — 33× in this paper · explained in 2 episodes
• Fine-Tuning — 22× in this paper · explained in 5 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Adaptation — 7× in this paper · explained in 1 episode
• Parameter-Efficient Fine-Tuning — 6× in this paper · explained in 2 episodes
• Quantization — 4× in this paper · explained in 12 episodes
• Alignment — 3× in this paper · explained in 1 episode
• F1 Score — 2× in this paper · explained in 3 episodes
• LoRA Adapters — 2× in this paper · explained in 3 episodes
• alpha — 2× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes

Sources

• Understanding Revision Behavior in Adaptive Writing Support Systems for Education

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