BERT4ItemSeg reaches macro-F1 of 0.9825 on core 10-K items across 3,737 annotated reports, outperforming GPT4ItemSeg (0.9567) and baselines.
RESULTS OF OPERATIONS
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
SinFormer is a tailored transformer that applies multi-scale self-attention and staged training to improve accuracy and robustness in radio frequency fingerprint identification on real-world data.
citing papers explorer
-
Utilizing Pre-trained and Large Language Models for 10-K Items Segmentation
BERT4ItemSeg reaches macro-F1 of 0.9825 on core 10-K items across 3,737 annotated reports, outperforming GPT4ItemSeg (0.9567) and baselines.
-
SinFormer: A Tailored Transformer for Robust Radio Frequency Fingerprint Identification
SinFormer is a tailored transformer that applies multi-scale self-attention and staged training to improve accuracy and robustness in radio frequency fingerprint identification on real-world data.