The Evolution of Natural Language Processing

Intro

After 2010s, neural networks and deep learning revolutionized Natural Language Processing (NLP). Here’s a brief overview of the key milestones in NLP’s evolution:

Word Embeddings

Techniques like Word2Vec and GloVe for representing words as meaningful vectors become foundational.

wevi: word embedding visual inspector

The Microsoft Azure OpenAI Embedding API service is fundamentally built upon the concept of word embeddings. Here’s how they connect:

• Core of Embeddings: Word embeddings (like those generated by Word2Vec, GloVe, or other models) are essential for the Azure OpenAI Embedding API to function. These pre-trained embeddings provide the numerical representations of words that capture their meaning and relationships to other words.

• The Azure OpenAI Advantage:

  • Fine-tuned Embeddings: Azure OpenAI likely uses large-scale, advanced embedding models (potentially similar to those used in GPT-3) that have been fine-tuned for creating embeddings specifically designed for similarity search and other downstream NLP tasks.
  • Focus on Semantic Similarity: The API is optimized to calculate the similarity between pieces of text. This relies heavily on the quality of word embeddings and likely involves comparing the embeddings of different text fragments.

How the Embeddings are Used:

1. Text Input: You provide a piece of text to the Azure OpenAI Embedding API.
2. Tokenization: The text is likely tokenized (broken down into words or other units, as we discussed earlier).
3. Embedding Retrieval: Each token is mapped to its corresponding word embedding from the pre-trained model.
4. Embedding Calculation: The individual word embeddings are likely combined and processed to produce a single embedding vector that represents the entire input text.
5. Similarity Scores: The API can then compare these embedding vectors to calculate how similar different pieces of text are to each other.

Important Point: While the Azure OpenAI embedding service relies on word embeddings as its foundation, it likely incorporates additional techniques and optimizations to excel at tasks like semantic similarity search.

Recurrent Neural Networks

Models like LSTMs capture long-range dependencies in sequences, improving machine translation and text generation.

RNNs and their Limitations:

• Sequential Processing: RNNs process data sequentially, one element at a time. This can be slow and struggle with capturing long-range dependencies between words in a sentence.
• Vanishing/Exploding Gradients: Training RNNs can be hampered by vanishing or exploding gradients, making it difficult to learn long-term dependencies effectively.

The Transformer Triumph

The Transformer architecture (introduced in the “Attention is All You Need” paper) supercharges NLP. Transformers excel at handling long sequences and relationships within text.

Large Language Models

Enormous models like GPT-3 showcase amazing generation and reasoning capabilities.