aiqbench analysis
This study investigates continued pretraining of FinBERT on unlabeled Finnish histopathological data, revealing distinct loss curve behaviors across domains and correlating these signals with downstream classification performance. By leveraging proxy datasets to validate findings without ground truth labels, the work advances methodological understanding for NLP in low-resource healthcare settings. It contributes directly to the machine learning literature by optimizing transformer adaptation strategies for specialized medical text.
Continued Pretraining of FinBERT on Finnish Histopathological Reports: Train-Time Signals and Proxy Downstream Correlations
arXiv:2604.14815v2 Announce Type: replace Abstract: In Natural Language Processing (NLP) classification tasks where a lack of labeled data is an issue, continued pretraining (CPT) of transformer models on unlabeled data is an established approach. In this paper, we have two aims. (1) We describe our observations from continued pretraining of the Finnish BERT transformer model (FinBERT) on a Finnish histopathological dataset (below, \emph{the Histopathology data}). (2) Since the Histopathology data has no classification labels, we gather public Finnish datasets as proxy data to analyze whether the signals observed in (1) are associated with downstream classification gains. We observe that CPT train-time loss curves differ strongly by domain, and that, in an exploratory analysis, certain CPT-derived features correlate with proxy classification improvement. In particular, this report contributes to the limited literature on NLP for Finnish healthcare data.
Abstract:In Natural Language Processing (NLP) classification tasks where a lack of labeled data is an issue, continued pretraining (CPT) of transformer models on unlabeled data is an established approach. In this paper, we have two aims. (1) We describe our observations from continued pretraining of the Finnish BERT transformer model (FinBERT) on a Finnish histopathological dataset (below, \emph{the Histopathology data}). (2) Since the Histopathology data has no classification labels, we gather public Finnish datasets as proxy data to analyze whether the signals observed in (1) are associated with downstream classification gains. We observe that CPT train-time loss curves differ strongly by domain, and that, in an exploratory analysis, certain CPT-derived features correlate with proxy classification improvement. In particular, this report contributes to the limited literature on NLP for Finnish healthcare data.
Computation and Language (cs.CL)
arXiv:2604.14815 [cs.CL]
(or arXiv:2604.14815v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2604.14815 Focus to learn more arXiv-issued DOI via DataCite
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