LLMs are applied in a generative pipeline for extracting, normalizing, and interpreting eligibility criteria from securities prospectuses, achieving up to 91% precision in document-level decisions with a conservative bias.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Fine-tuned multilingual LLMs achieve top shared-task scores on financial causality extraction in English and Spanish.
citing papers explorer
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LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank
LLMs are applied in a generative pipeline for extracting, normalizing, and interpreting eligibility criteria from securities prospectuses, achieving up to 91% precision in document-level decisions with a conservative bias.
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Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026
Fine-tuned multilingual LLMs achieve top shared-task scores on financial causality extraction in English and Spanish.