The paper investigates confidence estimation and selective prediction for financial named entity recognition (NER) under domain shift, using a stress test across SEC filings, financial news, and social media. It evaluates BERT and LoRA‑tuned Qwen2.5 models with five inference‑time confidence signals, finding that whole‑output probability is a strong in‑domain error detector but weak out‑of‑domain, while entity‑span probability and self‑consistency remain robust. Abstention can dramatically reduce sentence error on high‑confidence in‑domain data, but offers limited benefit under extreme social‑media shift, suggesting a staged deployment that first detects severe distribution shift before applying confidence gating.
By Zihao Zheng, Baichuan Li, Junyi Yao, Jiayu Long
arXiv:2606. 18192v1 Announce Type: new Abstract: As high-quality public web corpora become increasingly exhausted, clean long-context documents have become a scarce and expensive source of training data for large language models (LLMs).
By Nick Bettencourt, Xiaowei Ding, Kay Giesecke
arXiv:2608. 04200v1 Announce Type: cross Abstract: Financial sentiment classifiers are commonly evaluated against human labels, but strong linguistic performance does not necessarily imply economically useful return predictability.
By Fusheng Luo
arXiv:2602. 07294v4 Announce Type: replace-cross Abstract: With the increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures.
By Yidong Jiang, Junrong Chen, Eftychia Makri, Jialin Chen, Peiwen Li, Ali Maatouk, Leandros Tassiulas, Eliot Brenner, Bing Xiang, Rex Ying
arXiv:2609.35864v1 Announce Type: new
Abstract: SEC 10-K filings contain substantial financial information that is not consistently captured in structured datasets, creating a missing-data problem af...
By Prisha Nair, Roee Shraga
arXiv:2606. 10392v1 Announce Type: new Abstract: Financial named-entity recognition (NER) is essential for translating unstructured financial reports and news into structured knowledge graphs.
By Wu Yuerong, Mingni Luo