6 papers
From Data to Device: ELMOD An Efficient German-First 2.7B Language Model for Mobile Inference
Darina Gold, Alexander Schwirjow, Viktor Haag +4
We present ELMOD - Efficient Language Model for On-Device Deployment - a compact (2.7B) German language model designed for efficient inference on resource-constrained hardware. ELM…
CEDAR: Context Engineering for Agentic Data Science
Rishiraj Saha Roy, Chris Hinze, Luzian Hahn +1
We demonstrate CEDAR, an application for automating data science (DS) tasks with an agentic setup. Solving DS problems with LLMs is an underexplored area that has immense market va…
Pre-Training LLMs on a budget: A comparison of three optimizers
Joel Schlotthauer, Christian Kroos, Chris Hinze +3
Optimizers play a decisive role in reducing pre-training times for LLMs and achieving better-performing models. In this study, we compare three major variants: the de-facto standar…
RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG
Rishiraj Saha Roy, Chris Hinze, Joel Schlotthauer +5
Conversational question answering (ConvQA) is a convenient means of searching over RDF knowledge graphs (KGs), where a prevalent approach is to translate natural language questions…
Evidence Contextualization and Counterfactual Attribution for Conversational QA over Heterogeneous Data with RAG Systems
Rishiraj Saha Roy, Joel Schlotthauer, Chris Hinze +3
Retrieval Augmented Generation (RAG) works as a backbone for interacting with an enterprise's own data via Conversational Question Answering (ConvQA). In a RAG system, a retriever…
Data Processing for the OpenGPT-X Model Family
Nicolo' Brandizzi, Hammam Abdelwahab, Anirban Bhowmick +19
This paper presents a comprehensive overview of the data preparation pipeline developed for the OpenGPT-X project, a large-scale initiative aimed at creating open and high-performa…