Reliable Financial Named Entity Recognition Under Domain Shift: Confidence Estimation and Selective Prediction
Financial AI systems often train information extractors on one textual register and deploy them across filings, news, and user-generated content, and standard F1 scores do not indicate which predictions remain safe to automate when that input distribution changes. We study confidence estimation and selective prediction for financial named entity recognition (NER) on a three-tier stress test spanning SEC filings, financial news, and general-topic social media as an extreme out-of-domain condition, evaluating a BERT tagger and LoRA-tuned Qwen2.5-0.5B/1.5B models with five inference-time confidence signals, three training seeds, and bootstrap intervals. Confidence rankings themselves change ...
| Authors | Zihao Zheng, Baichuan Li, Junyi Yao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 August 2026 |
| Last updated at source | 26 August 2026 |
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