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This paper proposes a schema-constrained document-level event argument extraction method using fine-tuned mid-sized open LLMs with LoRA, role-set injection, and deterministic decoding, achieving state-of-the-art results on MAVEN-ARG.
Proposes G²C-MT, a graph-guided context selection framework for document-level machine translation that models structured discourse dependencies via a lightweight discourse graph and depth-biased random walk, outperforming baselines on multiple LLMs.