Word Vector Relationships

Navigating the landscape of text representation, and grasping how words relate to each other in vector form is essential. In the upcoming video, Anup Surendran dives into the history of word vectors and takes a closer look at Google's groundbreaking Word2Vec project. Why are vector relationships so critical, and what biases do they bring?

Let's find out!

Here, Anup traces the evolution of word vectors, emphasizing the milestone that is Google's Word2Vec project. One of the standout features of Word2Vec is vector arithmetic, allowing us to reason about words mathematically.

For example, the vector equation "King - Man + Woman = Queen" showcases this property brilliantly.

The video also explores the role of word vector relationships in similarity search—a key capability in large language models. However, Anup discusses a very important component, i.e., the inherent biases in these major technological developments.

Understanding these relationships and their implications deepens our grasp of Large Language Models and equips us to use them more responsibly. 🌐

💡 A practical insight

You’ll often hear the “vector embeddings” and “word vectors” being used interchangeably in the context of LLMs. These vector embeddings are then stored in vector indexes, specialized data structures engineered to ensure rapid and relevant data access using these embeddings.

How to Choose the Right Vector Embeddings Model

Selecting the appropriate model for generating embeddings is an intriguing topic on its own. It's essential to recognize that this domain has no one-size-fits-all solution. A glance at this MTEB Leaderboard on Hugging Face reveals a variety of embedding models, each tailored for specific applications. Currently, OpenAI's text-embedding-ada-002 stands out as the go-to model for producing efficient vector embeddings from diverse data, whether structured or unstructured. We'll delve deeper into its utilization in our tutorials by the end of this course.