The paper introduces NORA, a noise‑robust tagging system for Financial Numerical Entity (FNE) attributes, addressing errors in XBRL‑derived labels and under‑emphasized attributes such as reporting‑time relation, measurement scale, and accounting sign. NORA employs task‑aware instance‑specific weighting to reduce the impact of noisy labels during training and proposes an evaluation protocol that filters noisy labels in real‑world test sets. A large benchmark of 11.4 million FNEs across 6.3 million sentences is constructed, and experiments show NORA outperforms prior models in mean Accuracy and Macro F1, especially for time‑relation prediction.
arXiv:2608. 19558v1 Announce Type: new Abstract: Financial AI systems often train information extractors on one textual register and deploy them across filings, news, and user-generated content, while standard F1 scores do not indicate which predictions remain safe to automate when the input distribution changes.
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.
arXiv:2610.19432v2 Announce Type: replace Abstract: Comparing financial disclosures across languages requires identifying shared accounting concepts and recovering all numerical facts that express th...
arXiv:2502. 15411v4 Announce Type: replace-cross Abstract: Accurate tagging of earnings reports can yield significant short-term returns for stakeholders.
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).
arXiv:2610.19240v1 Announce Type: new Abstract: Financial disclosures express the underlying relationships of double-entry accounting through unstructured text. While financial LLM benchmarks predomi...
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.
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...
arXiv:2607. 19259v1 Announce Type: cross Abstract: Financial statement fraud detection (FSFD) is crucial for market integrity but faces challenges from increasingly sophisticated schemes and under-utilized textual data in financial reports.
Financial disclosures contain numerical claims, temporal statements, entity references, policy commitments, and risk descriptions that may conflict in qualitatively different ways. Detecting a conflict is only the first step: review workflows may also need to determine its type, since numerical, temporal, referential, factual, and normative inconsistencies require different evidence and downstream checks.
arXiv:2607. 26368v1 Announce Type: cross Abstract: Financial disclosures contain numerical claims, temporal statements, entity references, policy commitments, and risk descriptions that may conflict in qualitatively different ways.
Attribute-value extraction is important for building high-quality product catalogs in e-commerce. Existing systems either rely on generative or tagging strategies for extracting the attribute names an...