RLGT is a modular reinforcement learning framework for extremal graph theory that handles undirected, directed, looped, and multi-colored graphs to facilitate future research.
International Journal of Computer Vision , year =
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SGER applies a two-phase curriculum to fine-tune LLMs for name matching, reporting 99.02% accuracy and 0.994 F1 on 50,000 real-world Indian name pairs while outperforming baselines and deploying in production.
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RLGT: A reinforcement learning framework for extremal graph theory
RLGT is a modular reinforcement learning framework for extremal graph theory that handles undirected, directed, looped, and multi-colored graphs to facilitate future research.
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Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts
SGER applies a two-phase curriculum to fine-tune LLMs for name matching, reporting 99.02% accuracy and 0.994 F1 on 50,000 real-world Indian name pairs while outperforming baselines and deploying in production.