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PublicationUncovering Inconsistencies and Contradictions in Financial Reports using Large Language Models( 2023-12)
;Leonhard, David ;Berger, Armin ;Khaled, Mohamed ;Heiden, Sarah ;Dilmaghani, Tim ;Kliem, Bernd ;Loitz, RüdigerCorrect identification and correction of contradictions and inconsistencies within financial reports constitute a fundamental component of the audit process. To streamline and automate this critical task, we introduce a novel approach leveraging large language models and an embedding-based paragraph clustering methodology. This paper assesses our approach across three distinct datasets, including two annotated datasets and one unannotated dataset, all within a zero-shot framework. Our findings reveal highly promising results that significantly enhance the effectiveness and efficiency of the auditing process, ultimately reducing the time required for a thorough and reliable financial report audit. -
PublicationContradiction Detection in Financial Reports( 2023-01-23)
;Pucknat, Lisa ;Jacob, Basil ;Dilmaghani, Tim ;Nourimand, Mahdis ;Kliem, Bernd ;Loitz, RüdigerFinding and amending contradictions in a financial report is crucial for the publishing company and its financial auditors. To automate this process, we introduce a novel approach that incorporates informed pre-training into its transformer-based architecture to infuse this model with additional Part-Of-Speech knowledge. Furthermore, we fine-tune the model on the public Stanford Natural Language Inference Corpus and our proprietary financial contradiction dataset. It achieves an exceptional contradiction detection F1 score of 89.55% on our real-world financial contradiction dataset, beating our several baselines by a considerable margin. During the model selection process we also test various financial-document-specific transformer models and find that they underperform the more general embedding approaches.