About
Blog
Pills
Software
Trainings
Seminar
⚲
Search results
Large Language Models
Research feed
Pill
CLP-Transfer: Cross-Lingual and Progressive Transfer Learning
The CLP-Transfer method introduces a novel approach for cross-lingual language transfer by leveraging token overlap and a small pre-trained …
Pill
Position: Leverage Foundational Models for Black-Box Optimization
This paper explores the use of Large Language Models (LLMs) to address challenges in Black Box Optimization (BBO), particularly …
Pill
WECHSEL: Cross-Lingual Transfer
Language transfer enables the use of language models trained in one or more languages to initialize a new language model in another …
Seminar
RAGAR, Your Falsehood RADAR: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models
Mohammed Abdul Khaliq, MSc. Computational Linguistics Programm at the Institute for Natural Language Processing of the University of …
Blog
AutoDev: Exploring Custom LLM-Based Coding Assistance Functions
We explore the potential of custom code assistant functions based on large language models (LLMs). With our open-source software package …
Software
AutoDev: LLM-Based Coding Assistance Functions
AutoDev is a software package for the realisation of coding assistance functions using large language models (LLMs). It covers fine-tuning, …
Pill
Reasoning Traces as Learning Signal
An important feature of large language models is their ability to provide detailed responses that resemble “thinking step by …
Pill
Direct Preference Optimization
With direct preference optimization (DPO), a language model can be aligned with human preferences without using reinforcement learning, …
Pill
Augmented Language Models: a survey
A survey of recent advances in augmenting (large) language models with new capabilities such as reasoning, tool use, and more. While the …
Pill
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning
External knowledge can be injected into pre-trained language models (here specifically BERT) by training an additional language model on …
Other series in
Advances and fundamentals in ML
Simulation and AI
AI techniques are fundamentally transforming the field of simulation by combining physics-based modeling with data-driven machine learning.
Optimization in ML
Optimization is a key component of machine learning. In this series we review recent developments allowing to train larger models, faster …
Reinforcement Learning
Recent and popular advances in Reinforcement Learning are known to be data-hungry. Attempts to handle this deficit include developing …
Diffusion Models
Diffusion models (DM) have become the state of the art for sample quality in generative modelling. They work by sequentially corrupting …
Geometric deep learning
Specialized deep learning architectures exploit the intrinsic regularities arising from the underlying structure of the physical world. …
Check all
of our work
