16 citations · 44 across the 11 of their papers we have counts for
11 papers
Multi-Modal Vision vs. Text-Based Parsing: Benchmarking LLM Strategies for Invoice Processing
David Berghaus, Armin Berger, Lars Hillebrand +2
This paper benchmarks eight multi-modal large language models from three families (GPT-5, Gemini 2.5, and open-source Gemma 3) on three diverse openly available invoice document da…
Advancing Risk and Quality Assurance: A RAG Chatbot for Improved Regulatory Compliance
Lars Hillebrand, Armin Berger, Daniel Uedelhoven +7
Risk and Quality (R&Q) assurance in highly regulated industries requires constant navigation of complex regulatory frameworks, with employees handling numerous daily queries demand…
Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM
Lars Hillebrand, David Biesner, Christian Bauckhage +1
The DEDICOM algorithm provides a uniquely interpretable matrix factorization method for symmetric and asymmetric square matrices. We employ a new row-stochastic variation of DEDICO…
Pointer-Guided Pre-Training: Infusing Large Language Models with Paragraph-Level Contextual Awareness
Lars Hillebrand, Prabhupad Pradhan, Christian Bauckhage +1
We introduce "pointer-guided segment ordering" (SO), a novel pre-training technique aimed at enhancing the contextual understanding of paragraph-level text representations in large…
Informed Named Entity Recognition Decoding for Generative Language Models
Tobias Deußer, Lars Hillebrand, Christian Bauckhage +1
Ever-larger language models with ever-increasing capabilities are by now well-established text processing tools. Alas, information extraction tasks such as named entity recognition…
Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models
Lars Hillebrand, Armin Berger, Tobias Deußer +8
Auditing financial documents is a very tedious and time-consuming process. As of today, it can already be simplified by employing AI-based solutions to recommend relevant text pass…
sustain.AI: a Recommender System to analyze Sustainability Reports
Lars Hillebrand, Maren Pielka, David Leonhard +9
We present sustainAI, an intelligent, context-aware recommender system that assists auditors and financial investors as well as the general public to efficiently analyze companies'…
Towards automating Numerical Consistency Checks in Financial Reports
Lars Hillebrand, Tobias Deußer, Tim Dilmaghani +4
We introduce KPI-Check, a novel system that automatically identifies and cross-checks semantically equivalent key performance indicators (KPIs), e.g. "revenue" or "total costs", in…
KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents
Tobias Deußer, Syed Musharraf Ali, Lars Hillebrand +4
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…
KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports
Lars Hillebrand, Tobias Deußer, Tim Dilmaghani +4
We present KPI-BERT, a system which employs novel methods of named entity recognition (NER) and relation extraction (RE) to extract and link key performance indicators (KPIs), e.g.…
Towards Supervised Extractive Text Summarization via RNN-based Sequence Classification
Eduardo Brito, Max Lübbering, David Biesner +2
This article briefly explains our submitted approach to the DocEng'19 competition on extractive summarization. We implemented a recurrent neural network based model that learns to…