Linguaggio mascherato

Come parte del team di sviluppo AI di un'azienda di vendite, ti è stato chiesto di spiegare come funziona il masked language modeling agli stakeholder di business. Chiederai al gruppo di identificare le parole mancanti mascherate per questo processo di pre-training.

Il modello ha ricevuto dati non mascherati che contengono i feedback degli utenti sull'assistenza clienti e le varie problematiche che gli agenti di supporto hanno dovuto gestire.

Ecco un esempio di dati mascherati: The [MASK] support [MASK] quickly resolved the [MASK].

Quali parole sono state mascherate?

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Concetti sui Large Language Models (LLM)

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Concetti sui Large Language Models (LLM)

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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: Elementi fondamentali per addestrare gli LLM Exercise 2: Linguaggio mascherato

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Exercise 3: Predici la parola successiva Exercise 4: Costruire da zero Exercise 5: Introduzione al transformer Exercise 6: Relazioni tra parole distanti Exercise 7: Componenti del transformer Exercise 8: Meccanismi di attention Exercise 9: Focus della multi-head attention Exercise 10: Self vs. multi-head attention Exercise 11: Fine-tuning avanzato Exercise 12: Training end-to-end Exercise 13: Training, tuning e feedback Exercise 14: Costruire un 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