NLP & LLMs: Harnessing the power of language for economic and related research

Do you want to learn how to make use of text data for your research?

This in-person workshop is ideal for researchers who want to learn the potential of Natural Language Processing (NLP) and Large Language Models (LLMs) for economic and related research.

You will receive a broad overview of the theoretical foundations of NLP and its practical concepts. The workshop closes with a Coding Lab to apply this knowledge to real world problems.


A basic understanding of Machine Learning and Deep Learning is required.

Basic Python skills are required. Familiarity with common modules for text processing and deep learning frameworks is recommended.


09:30 – 11:00 NLP Basics (1-3)

11:15 – 12:45 Neural Nets & Transfer Learning (4-8)

13:45 – 15:45 Generative Models (9-End)

16:00 – 17:00 Coding Lab


1 Learning Paradigms

Understand the different learning paradigms, Relate type of learning to amount of labeled data required

2 NLP tasks

Understand the different types of tasks (low- vs. high-level), Purely Linguistic tasks vs. more general classification tasks

3 Word Embeddings

Understand what word embeddigns are, Learn the main methods for creating them

4 Recurrent Neural Networks

Understand recurrent structure of RNNs, Learn the different types of RNNs

5 Attention

Understand attention mechanism, Learn the different types of attention, The Transformer / Self-Attention

6 The BERT Architecture

Use of the transformer encoder in this model, Understand the pre-training, Gain understanding of the fine-tuning procedure Differences between token- and sequence classification

7 BERTology

Understand how impactful this architecture was, See how this changed research in the field, Glimpse into BERTology

8 Model distillation

soft vs. hard targets, understand how distillation works, DistilBERT, other approaches towards compression

9 Towards a unified task format  

developments of the post-BERT era, reformulating classification tasks, multi-task learning, fine-tuning on task-prefixes

10 GPT series

use of the transformer decoder, input modifications (and how this is useful), concept of prompting