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Existing studies primarily focus on concept name prediction and face two important limitations. First, labels derived from inline XBRL may contain errors because filings are usually prepared manually. Second, other important FNE attributes, such as reporting-time relation, measurement scale, and accounting sign, are less emphasized. We propose \\textbf{NO}ise-\\textbf{R}obust Tagging for Rich Financial Numerical Entity \\textbf{A}ttributes (\\textsc{NORA}) to address these gaps. NORA uses task-aware instance-specific weighting to attenuate the influence of noisy labels during training, and we further propose the Neighborhood Prior-adjusted KNN (NPK) filtering method for more reliable evaluation on real-world noisy test sets. In addition, we construct a large-scale benchmark containing 6.6 million instances with multi-attribu","title":"Noise-Robust Financial Numerical Entity Attribute Tagging","url":"https://arxiv.org/abs/2605.24910","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.24910v1 Announce Type: new \nAbstract: Financial Numerical Entity (FNE) understanding aims to recover the meaning of numerical mentions in financial reports. Existing studies primarily focus on concept name prediction and face two important limitations. First, labels derived from inline XBRL may contain errors because filings are usually prepared manually. Second, other important FNE attributes, such as reporting-time relation, measurement scale, and accounting sign, are less emphasized. We propose \\textbf{NO}ise-\\textbf{R}obust Tagging for Rich Financial Numerical Entity \\textbf{A}ttributes (\\textsc{NORA}) to address these gaps. NORA uses task-aware instance-specific weighting to attenuate the influence of noisy labels during training, and we further propose the Neighborhood Prior-adjusted KNN (NPK) filtering method for more reliable evaluation on real-world noisy test sets. 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