G^2C-MT frames DocMT context selection as path discovery on a discourse graph with semantic, adjacency, and keyword edges, using depth-biased random walks to sample context for LLM translation and reports outperformance on multiple models.
Efficiently exploring large language mod- els for document-level machine translation with in-context learning.arXiv preprint arXiv:2406.07081,
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G^2C-MT: Graph-Guided Context Selection for Document-Level Machine Translation
G^2C-MT frames DocMT context selection as path discovery on a discourse graph with semantic, adjacency, and keyword edges, using depth-biased random walks to sample context for LLM translation and reports outperformance on multiple models.