Prof. Dr.-Ing.

Bauckhage, Christian

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  • Publication
    Uncovering Inconsistencies and Contradictions in Financial Reports using Large Language Models
    ( 2023-12)
    Deußer, Tobias orcid-logo
    ;
    Leonhard, David
    ;
    Hillebrand, Lars Patrick
    ;
    Berger, Armin
    ;
    Khaled, Mohamed
    ;
    Heiden, Sarah
    ;
    Dilmaghani, Tim
    ;
    Kliem, Bernd
    ;
    Loitz, Rüdiger
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    Correct 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.
  • Publication
    Contradiction Detection in Financial Reports
    ( 2023-01-23)
    Deußer, Tobias orcid-logo
    ;
    Pielka, Maren
    ;
    Pucknat, Lisa
    ;
    Jacob, Basil
    ;
    Dilmaghani, Tim
    ;
    Nourimand, Mahdis
    ;
    Kliem, Bernd
    ;
    Loitz, Rüdiger
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    Finding 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.