Prof. Dr.-Ing.

Bauckhage, Christian

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Now showing 1 - 3 of 3
  • 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
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    Jacob, Basil
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    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.
  • Publication
    KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents
    ( 2022-12)
    Deußer, Tobias orcid-logo
    ;
    Ali, Syed Musharraf
    ;
    Hillebrand, Lars Patrick
    ;
    Nurchalifah, Desiana Dien
    ;
    Jacob, Basil
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where the main objective is to extract Key Performance Indicators (KPIs) from financial documents and link them to their numerical values and other attributes. We further provide four accompanying baselines for benchmarking potential future research. Additionally, we propose a new way of measuring the success of said extraction process by incorporating a word-level weighting scheme into the conventional F 1 score to better model the inherently fuzzy borders of the entity pairs of a relation in this domain.