NEST is a new benchmark dataset for narrative event structures in long videos, with baselines reporting ETD below 8%, EL under 6%, EAE below 11%, and ERE at 35-44% F1.
An Improved Baseline for Sentence-level Relation Extraction
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
DistilledGemma uses prompt engineering, QLoRA fine-tuning on a large teacher, and response-level distillation to a small student, ranking 3rd and 2nd in a 2026 historical relation extraction shared task while keeping the deployed model at ~2.3B parameters.
citing papers explorer
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NEST: Narrative Event Structures in Time for Long Video Understanding
NEST is a new benchmark dataset for narrative event structures in long videos, with baselines reporting ETD below 8%, EL under 6%, EAE below 11%, and ERE at 35-44% F1.
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DistilledGemma: Balanced Efficiency-Accuracy for Person-Place Relation Extraction from Multilingual Historical Articles
DistilledGemma uses prompt engineering, QLoRA fine-tuning on a large teacher, and response-level distillation to a small student, ranking 3rd and 2nd in a 2026 historical relation extraction shared task while keeping the deployed model at ~2.3B parameters.