Automatic Domain Classification of Tabular Datasets Using Large Language Models

Optimizing sample selection for large language model-based entity matching using AssistEM

AssistEM, a framework for efficient LLM adaptation to EM via principled data selection, demonstrates that selective fine-tuning not only accelerates adaptation but also improves training efficiency (requiring fewer GPU hours), enabling open-source LLMs to rival–and in some cases outperform–closed-source models.

Research on Text Information Extraction and Imbalanced Classification Methods for Enterprise Profiling

A comprehensive natural language processing (NLP) pipeline for extracting key information and identifying industries, which outperforms traditional machine learning baselines and single deep learning models, offering more reliable recognition for minority classes.

A German-Language Dataset and Annotation Tool for Tabular Question Answering

A dataset and annotation tool to support the development of German-language Tabular Question Answering systems, with a specific focus on sustainability-related information, exclusive use of the German language and a strong emphasis on information retrieval from tables embedded in sustainability reports.

Detecting reconciliation discrepancies in tabular data using transformers

Introduction Large organisations maintain heterogeneous datasets in data lakes, where schema variability, inconsistent formats, and semantic ambiguity complicate reconciliation. Achieving a unified view of entities requires methods that capture both structural equality and semantic relationships. Methods We propose a t...

Scientific Article Section Classification (SASC) Dataset

Augmenting Datasets for Fine-Tuning Large Language Models Using Semantic Variations

This study explores a semantic variation methodology to augment training data by generating question-answer pairs with explicit control over semantic similarity, and shows that semantically controlled augmentation improves domain-specific knowledge acquisition while preserving consistency.