Word embeddings vs bag-of-words

Sei un Machine Learning Engineer e hai notato che l’attuale modello, che usa un approccio bag-of-words, spesso fraintende il significato di un testo, producendo risultati imprecisi. Considerando questo problema, stai valutando di passare ai word embeddings per migliorare le prestazioni del modello.

Quale delle seguenti opzioni descrive meglio perché i word embeddings potrebbero essere una scelta migliore per superare il problema di interpretazione errata riscontrato con il modello bag-of-words?

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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: Novità degli LLM Exercise 2: Problem solving con gli LLM Exercise 3: Modelli tradizionali vs. LLM Exercise 4: Panoramica generale dell'NLP Exercise 5: Preparazione dei dati Exercise 6: Preprocessing e rappresentazione del testo Exercise 7: Word embeddings vs bag-of-words

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Exercise 8: Fine-tuning Exercise 9: Sfide nella costruzione degli LLM Exercise 10: Adatta un modello pre-addestrato Exercise 11: Pre-trained o fine-tuned?Exercise 12: Tecniche di apprendimento Exercise 13: Perfeziona (fine-tune) un modello 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