Traditionele modellen vs. LLM's

LLM's presteren beter dan traditionele taalmodellen doordat ze complexe taalpatronen begrijpen en snel nieuwe taken leren. In tegenstelling tot traditionele modellen, die moeite hebben met de betekenis achter woorden en zinnen, kunnen LLM's makkelijk context en relaties tussen woorden herkennen. Dat maakt ze een uitstekende keuze voor veel NLP-toepassingen.

Bepaal de kenmerken van Large Language Models versus traditionele taalmodellen, zodat je voor een bepaald probleem de juiste aanpak kunt kiezen.

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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: De nieuwheid van LLM's Exercise 2: Problemen oplossen met LLM's Exercise 3: Traditionele modellen vs. LLM's

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Exercise 4: Algemeen overzicht van NLP Exercise 5: Datavoorbereiding Exercise 6: Tekstvoorbewerking en -representatie Exercise 7: Word embeddings boven bag-of-words Exercise 8: Fine-tuning Exercise 9: Uitdagingen bij het bouwen van LLM’s Exercise 10: Pas een voorgetraind model aan Exercise 11: Pre-trained of fine-tuned?Exercise 12: Leertechnieken Exercise 13: Een model fine-tunen 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: Data concerns and considerations Exercise 2: Is your model fair?Exercise 3: Un-biased and relevant Exercise 4: Customer service of a bank Exercise 5: Ethical and environmental concerns Exercise 6: Responsible use Exercise 7: Ethics and environment Exercise 8: Where are LLMs heading?Exercise 9: Creativity vs. efficiency Exercise 10: Analyzing literary works Exercise 11: Time to wrap-up