LLMs in action Modern transformer-based LLMs such as GPT or Bard are based on a statistical analysis of the co-occurrence of tokens or words. To do this, texts and data are broken down into tokens for machine processing and positioned in semantic spaces using vectors. Vectors can also be whole words (Word2Vec), entities (Node2Vec), and attributes. In semantics, the semantic space is also described as an ontology. Since LLMs rely more on statistics than semantics, they are not ontologies. However, the AI gets closer to semantic understanding due to the amount of data.
AI is based on the determination
How are these recommendations made? Suggestions from Bing Chat and other generative AI tools are always contextual. The AI mostly uses DB to Dataneutral secondary sources such as trade magazines, news sites, association and public institution websites, and blogs as a source for recommendations. The output of generative of statistical frequencies. The more often words appear in sequence in the source data, the more likely it is that the desired word is the correct one in the output. Words frequently mentioned in the training data are statistically more similar or semantically more closely related. Which brands and products are mentioned in a certain context can be explained by the way LLMs work.
Hyundai and Chevrolet models
For example, if you search Bing. Chat for the best running shoes for a 96-kilogram. Runner who runs 20 kilometers per week, Brooks, Saucony. Hoka and New Balance BA Leads shoes will be suggested. Bing Chat – running shoes query When you ask Bing Chat for safe, family-friendly cars that are big enough for shopping and travel, it suggests Kia, Toyota, Bing Chat – family-friendly cars query The approach of potential methods such as LLM optimization is to give preference to certain brands and products when dealing with corresponding transaction-oriented questions.