Schema-Constrained Document-Level Event Argument Extraction with Lightweight LLM Fine-Tuning
Researchers introduce a method for document-level event argument extraction that leverages fine-tuned mid-sized open large language models (LLMs). Their approach integrates role-set injection, LoRA-based parameter-efficient fine-tuning, and deterministic decoding with schema validation, achieving 42.39% F1 on the MAVEN-ARG benchmark with Phi-4 (14B). This method outperforms previously reported GPT baselines on multiple evaluation metrics.
Why it matters: The work shows that accessible, fine-tuned open LLMs can surpass proprietary models on complex information extraction tasks, potentially broadening access to high-quality structured data extraction.
Full story at: arXiv Computation and Language ↗