Tutorials

9.00 AM, 6 September (Saturday),
coffee break 10.50 – 11.20 AM

Tharindu Ranasinghe and Damith Premasiri:
Legal NLP in the LLM era

This tutorial examines the transformation of Legal NLP in the era of large language models, beginning with key principles of task formulation and data preparation. We will discuss retrieval and judgment prediction in detail, exploring their methodologies, challenges, and applications in legal contexts. We conclude with a forward-looking discussion on the future of Legal AI and the ethical considerations surrounding its applications in the practice of law.

2.00 AM, 6 September (Saturday),
coffee break 3.50 – 4.20 PM

Burcu Can Buglalilar:
From Large to Small: Building Affordable Language Models with Limited Resources

This tutorial aims to question the limitations and harms of Large Language Models, followed by a comprehensive review of Small Language Models, covering prominent examples, their key techniques, and their capabilities. It will also give an overview of even smaller ‘baby’ language models. Finally, the tutorial will conclude by presenting some recent studies in which we developed baby language models using a very small amount of data.

9.00 AM, 7 September (Sunday),
coffee break 10.50 – 11.20 AM

Anna Rogers and Max Müller-Eberstein:
Studying Generalization in the Age of Contamination

The tutorial will discuss the challenges of doing NLP research in the age of LLMs, when we can no longer be sure that the test data was not observed in training. We will cover the main approaches to studying generalization in various settings, and present a new framework for working with controlled test-train splits across linguistically annotated data at scale.

2.00 AM, 7 September (Sunday),
coffee break 3.50 – 4.20 PM

Salima Lamsiyah:
AI Content in NLP: Trends, Detection, and Applications

This tutorial provides a comprehensive overview of AI-generated content in Natural Language Processing (NLP). It covers recent trends in text generation, methods for detecting AI-generated text, and practical applications of such content. The content includes an exploration of state-of-the-art models and techniques for text generation, approaches to identifying machine-generated text, a review of key benchmarks and datasets, and a discussion of open research challenges.