¿Cómo entienden los LLM las palabras con significados múltiples?

Embeddings! They are ways to convert words, sentences or even full fledged documents into numerical representations (vectors) that computers can understand. You can think of embeddings as translating human language into a form that machines can easily process and analyze.

To better understand embeddings, let’s start with a simple analogy. Let’s say you are arranging books in a library. You might place books that have a similar topic next to each other. For instance you might put science books on one shelf and cooking books on another. Embeddings work in a similar fashion by placing similar words or concepts with similar meaning close together in a multidimensional space.

How do Embeddings Work (Simply Explained):

1. Turn words into numbers:
   • Each word is given a unique set of numbers (a vector). Words with similar meanings have similar vectors.
2. Measuring Similarity:
   • By calculating the distance between vectors, AI models can understand if concepts or words are related. Closer vectors mean more similarity.

Practical Examples of Embeddings:

1. Semantic Search:
   • When you search for a term that has many meanings, for example “apple”, it’s going to distinguish between the company and the fruit based on context.
2. Recommendation Systems:
   • Platforms like Amazon or Netflix use embeddings to suggest items similar to movies or products you’ve previously enjoyed.
3. Customer Segmentation:
   • Grouping customers based on behaviour or interests to better customer experiences and enable personalized marketing.

Business Value of Embeddings:

• Improve efficiency in retrieving relevant information quickly.
• Personalize recommendations, enhancing customer satisfaction and engagement.
• Gain deeper insights into customer behaviour through effective data clustering.

Understanding embeddings helps to leverage Generative AI more effectively. As we’ve discussed, embeddings are very powerful and help businesses make more informed decisions based on data. I hope this article proved useful in bettering your understanding of embeddings in Generative AI !