Creates a Bangla event detection benchmark with clean, ASR, and corrupted text variants and finds decoder-only LLMs more robust to noise than encoder models.
Guideline Learning for In-Context Information Extraction
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
citing papers explorer
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Beyond Clean Text: Evaluating Encoder and Decoder Robustness for Bangla Event Detection in Noisy Text
Creates a Bangla event detection benchmark with clean, ASR, and corrupted text variants and finds decoder-only LLMs more robust to noise than encoder models.
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Task Decomposition for Efficient Annotation
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.