Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text
Quick Answer
This study investigates fine-grained inconsistency classification in financial disclosures, achieving 65.3% accuracy with a fine-tuned 300M encoder.
Quick Take
The research highlights the challenges of localization quality, particularly for referential inconsistencies, and suggests that better evidence extraction and reasoning are essential for progress.
Key Points
- Fine-tuned 300M encoder achieved 65.3% accuracy on SBID-FD benchmark.
- Referential inconsistencies are highly sensitive to localization quality.
- Factual and logical inconsistencies remain challenging even with provided evidence.
- Localization errors and type-discrimination errors are distinct issues.
- Improving evidence extraction is crucial for better inconsistency classification.
Paper Resources
Source Excerpt
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. We study this problem as fine-grained inconsistency classification. Using a fixed 5,940-ins
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