Creativiteit vs. efficiëntie

Onderzoekers in AI verwachten dat toekomstige doorbraken in LLMs meerdere gebieden zullen omvatten, zoals verklaarbaarheid van modellen, rekenkundige efficiëntie, minder supervisie en meer creativiteit.

Als AI-onderzoeker word je gevraagd de volgende mogelijke onderzoeksprojecten in twee categorieën in te delen, rekening houdend met de zojuist genoemde toekomstige ontwikkelgebieden voor LLMs.

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Concepten van Large Language Models (LLMs)

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Concepten van Large Language Models (LLMs)

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The AI landscape is evolving rapidly, and Large Language Models (LLMs) are at the forefront of this evolution. This chapter examines how LLMs are advancing the development of human-like artificial intelligence and transforming industries through their numerous applications. You will explore the challenges and complexity associated with language modeling.

Exercise 1: The rise of LLMs in the AI landscape Exercise 2: Definition of an LLM Exercise 3: LLMs in the AI landscape Exercise 4: AI vs. LLM applications Exercise 5: Real-world applications Exercise 6: Business applications Exercise 7: Multimodal applications Exercise 8: Automate data-driven tasks Exercise 9: Challenges of language modeling Exercise 10: What can a language model do?Exercise 11: Single vs. multi-task learning

This chapter emphasizes the novelty of LLMs and their emergent capabilities while outlining various NLP techniques for data preparation. You will learn the challenges of training LLMs and how fine-tuning can effectively address them. You will also understand how N-shot learning techniques enable efficient adaptation of pre-trained models when faced with limited labeled data.

Exercise 1: Novelty of LLMs Exercise 2: Problem solving with LLMs Exercise 3: Traditional models vs. LLMs Exercise 4: Generalized overview of NLP Exercise 5: Data preparation Exercise 6: Text preprocessing and representation Exercise 7: Word embeddings over bag-of-words Exercise 8: Fine-tuning Exercise 9: Challenges in building LLMs Exercise 10: Adapt a pre-trained model Exercise 11: Pre-trained or fine-tuned?Exercise 12: Learning techniques Exercise 13: Fine-tune a model Exercise 14: N-shot learning

In this chapter, you will learn about the fundamental building blocks of training an LLM, such as pre-training techniques. You'll also gain an intuitive understanding of complex concepts like transformer architecture, including the attention mechanism. The chapter discusses an advanced fine-tuning technique and summarizes the training process to complete an LLM.

Exercise 1: Building blocks to train LLMs Exercise 2: Masked language Exercise 3: Predict the next word Exercise 4: Building from scratch Exercise 5: Introducing the transformer Exercise 6: Relationships between distant words Exercise 7: Transformer components Exercise 8: Attention mechanisms Exercise 9: Focus of multi-head attention Exercise 10: Self vs. multi-head attention Exercise 11: Advanced fine-tuning Exercise 12: End-to-end training Exercise 13: Training, tuning & feedback Exercise 14: Building an LLM

In this chapter, we delve into the key considerations when training LLMs, such as large data availability, data quality, accurate labeling, and the implications of biased data. You will also examine various LLM risks like data privacy, ethical concerns, and environmental impact. Lastly, the chapter concludes by discussing emerging research areas and the evolving landscape of LLMs.

Exercise 1: Zorgen en aandachtspunten rond data Exercise 2: Is je model eerlijk?Exercise 3: Onbevooroordeeld en relevant Exercise 4: Klantservice van een bank Exercise 5: Ethische en milieukundige bezorgdheden Exercise 6: Verantwoord gebruik Exercise 7: Ethiek en milieu Exercise 8: Waar gaan LLM's naartoe?Exercise 9: Creativiteit vs. efficiëntie

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Exercise 10: Literaire werken analyseren Exercise 11: Tijd om af te ronden