Zimu Wang

Narrative Nexus at SemEval-2026 Task 4: Modeling Narrative Similarity via Instruction-Based Fine-Tuning and Synthetic Data Augmentation

This paper addresses SemEval-2026 Task 4 Track A: Narrative Story Similarity by reformulating it as an instruction-following generation problem, employing parameter-efficient fine-tuning via LoRA to adapt pretrained large language models for triplet-based narrative comparison and incorporating synthetic triplet samples generated by a large language model for data augmentation.