Named Entity Recognition (NER) in the legal domain presents unique challenges due to specialized terminology and complex linguistic structures inherent in legal texts. While large language models (LLMs) like GPT-4, Llama-3, and others have significantly advanced natural language processing, their effectiveness in domain-specific tasks like legal Named Entity Recognition remains underexplored. This study conducts a comprehensive comparative analysis of eleven state-of-the-art LLMs on legal NER tasks across seven diverse datasets in five languages, namely English, Portuguese, German, Turkish, and Ukrainian. We evaluate the models’ performance using F1scores, focusing on their ability to accurately identify and classify legal entities. Our findings reveal significant variability in LLM performance across different languages and legal contexts, with proprietary models like GPT-4 achieving the highest overall scores. The results highlight the influence of model architecture, dataset characteristics, and prompt design on the effectiveness of legal NER tasks. This study provides valuable benchmarks for legal NER applications and offers insights into the strengths and limitations of current LLMs, guiding future research and development in legal natural language processing.

We explore the use of Large Language Models (LLMs) for automating the extraction of Key Performance Indicators (KPIs) from diverse financial reports without any additional fine-tuning. We focus on evaluating various proprietary and open-source LLMs to address the joint named entity recognition and relation extraction tasks essential for accurately linking KPIs to their corresponding values and attributes. Our study highlights the technical challenges involved in the extraction process and presents a comprehensive evaluation of the models’ effectiveness. Our results reveal significant insights into handling these LLMs in such a crucial environment and showcase the transformative potential of LLMs in enhancing financial analysis and decision-making.

Risk and Quality (R&Q) assurance in highly regulated industries requires constant navigation of complex regulatory frameworks, with employees handling numerous daily queries demanding accurate policy interpretation. Traditional methods relying on specialized experts create operational bottlenecks and limit scalability. We present a novel Retrieval Augmented Generation (RAG) system leveraging Large Language Models (LLMs), hybrid search and relevance boosting to enhance R&Q query processing. Evaluated on 124 expert-annotated real-world queries, our actively deployed system demonstrates substantial improvements over traditional RAG approaches. Additionally, we perform an extensive hyperparameter analysis to compare and evaluate multiple configuration setups, delivering valuable insights to practitioners.

Table structure recognition (TSR), the task of inferring the layout of tables, including the row, column, and cell structure, is a surprisingly complex task. With the growing amount and importance of digital documents, it has become an increasingly relevant problem, which nonetheless has not yet been solved adequately and still presents a very active area of research. In recent years, a growing number of deep-learning-based approaches to table parsing have been proposed.This paper presents a novel deep-learning-based table structure recognition method that can predict row, column, and cell bounds for table images with a high degree of accuracy. To achieve this goal, a multi-stage pipeline incorporating a Vision-Transformer-based Autoencoder model was devised. This model was trained to predict cell regions for table images, from which accurate cell bounds can be inferred, including spanning cells which cover multiple rows or columns. The goal was to obtain a model that generalizes well and can return accurate predictions on various tables of differing complexity, even if they contain little initial structural information.An additional modification to the model architecture presented in the Masked Autoencoder (MAE) approach was also evaluated.