Lecture Series on AI and Large Language Models

A Short Introduction to Efficient Natural Language Processing

Abstract:

The lecture focuses on the challenges of developing efficient models with limited data and resources. It addresses strategies for maximizing data and model efficiency, particularly for large, computationally intensive models that are often based on English data. It discusses techniques such as prefiltering, online methods, data enrichment, curriculum learning, and parameter-efficient approaches such as adapters, prompt tuning, and prefix tuning to boost performance without extensive data requests.

TrustLLM - Towards Trustworthy and Factual Language Models

Abstract

This talk gives an overview of the EU project ‘TrustLLM’, which aims to develop open, reliable and neutral Large Language Models (LLMs). The initial focus is on Germanic languages. This will create the basis for an advanced, open platform that supports modular and extensible next-generation European LLMs.

ChatGPT in Action: Enhancing Your Workflow

Abstract

Many users work with ChatGPT without fully exploiting its potential. This lecture will show you how prompt engineering can help you to use applications like ChatGPT, which are based on Large Language Models (LLMs), more effectively.

The event is aimed at both beginners who have no experience with ChatGPT and those who want to learn more about best practices, possible challenges and limitations of this technology.

Challenges and Threats in Generative AI: Exploits and Misuse

Abstract

This talk will discuss the security challenges associated with generative AI in more detail. These fall into two possible categories: firstly, manipulated inputs and, secondly, the misuse of computer-generated results.

On New Security and Safety Challenges Posed by LLMs and How to Evaluate Them

Abstract

This online lecture explores the widespread integration of Large Language Models (LLMs) into various real-world applications, highlighting both the vast opportunities for aiding diverse tasks and the significant challenges related to security and safety. Unlike earlier models with static generation, the dynamic, multi-turn, and adaptable nature of LLMs presents substantial difficulties in achieving robust evaluation and control. Join us as we examine the emerging risks associated with LLMs, delve into methodologies for rigorous evaluation, and tackle the complex challenges involved in implementing effective mitigations.

Contextualizing LLMs – What are the social and ethical implications?

Abstract

This talk focuses on the social implications of the widespread use of large-scale language models. Steffen Albrecht discusses how generative AI could change public debate, administration and the arts, and how politics and society can steer these developments. After all, in order to recognize the potential and limitations of current AI systems and find ways to improve them, we need to look beyond their technological functions and consider the context of their development and use.

Ethical Considerations on Hate Speech and AI

Inhaltszusammenfassung

In this presentation, the speaker will delve into ethical dilemmas surrounding the handling of hate speech. The primary emphasis will be on exploring how AI can play a role in curbing the dissemination of hate speech and the potential challenges it poses. The discussion will distinguish between two key realms: hate speech prevalent on social media platforms and that which emerges from Large Language Models (LLMs).

Within these realms, there are distinct concerns to address. On social media platforms, hasty intervention may inadvertently lead to censorship, while with LLM-generated hate speech, there's a looming threat of stifling innovation. Thus, the imperative lies in identifying ethically sound compromises and devising technical mechanisms for their effective implementation.

ChatGPT's Backgrounds: Exploring the World of Large Language Models

Abstract

In this talk, various key aspects of training LLMs will be explained in detail. This includes discussions on the architecture of such models, the selection and fine-tuning of training data, and the challenges and approaches to dealing with large datasets and computational resources. Current research trends and methodological innovations in the field of LLM training will also be discussed.

In addition, various exemplary applications and possible uses of LLMs will be presented during the lecture. This includes applications in natural language processing, automatic text generation, translation, sentiment analysis and many more. Practical case studies and success stories from industry and research will also be presented to illustrate the versatility and potential of LLMs.

An important part of the presentation is the current state of the technology and an outlook for the future. Current developments and trends in LLM research and application will be highlighted and potential challenges and opportunities that could arise in the coming years will be discussed. Possible areas of application beyond the current range of applications will also be considered in order to take a look at future developments and innovations in the field of speech technology.

Ally or Adversary: Examining the Impact of Large Language Models on Academics and Academic Work

Abstract:

This talk will analyse and present the impact of Large Language Models (LLMs) on science. Potential benefits and risks for the scientific system and the scientific profession will be discussed. Ethical issues and the role of LLMs in academic tasks will be highlighted using empirical data. In addition, the changing role of academics in the digital world will be discussed, including the datafication and quantification of academics and their institutions and future challenges.