I'm actually genuinely interested to know what you're referring to, because as an I.T. professional who's doing their best to keep up with how things work, that's more or less how I understood it as well. When words are mapped to a continuous vector space, placing semantically related words near one another gives their vector coordinates similar values (see the excerpt from IBM below).
However, I really don't understand how that necessarily enables one to perform arithmetic functions with two sets of vector coordinates and expect the result to be something tangential to the original two words. I understand how using a model to create embeddings with semantically correlated values can be achieved, and why that would be so fundamental for LLMs. My math skills aren't advanced enough to confidently land on either side of this question, but my instincts are that such an elegant relationship would be unlikely. Then again, mathematics are replete with counterintuitive but elegantly clever connections, so I could absolutely understand why this is eminently believable--especially in the context of AI and language models.
According to the descriptions from IBM [0]:
> Word embeddings capture the semantic relationships and contextual meanings of words based on their usage patterns in a given language corpus. Each word is represented as a fixed-sized dense vector of real numbers. It is the opposite of a sparse vector, such as one-hot encoding, which has many zero entries.
> The use of word embedding has significantly improved the performance of natural language processing (NLP) models by providing a more meaningful and efficient representation of words. These embeddings enable machines to understand and process language in a way that captures semantic nuances and contextual relationships, making them valuable for a wide range of applications, including sentiment analysis, machine translation and information retrieval.
> Popular word embedding models include Word2Vec, GloVe (Global Vectors for Word Representation), FastText and embeddings derived from transformer-based models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer).
However, I really don't understand how that necessarily enables one to perform arithmetic functions with two sets of vector coordinates and expect the result to be something tangential to the original two words. I understand how using a model to create embeddings with semantically correlated values can be achieved, and why that would be so fundamental for LLMs. My math skills aren't advanced enough to confidently land on either side of this question, but my instincts are that such an elegant relationship would be unlikely. Then again, mathematics are replete with counterintuitive but elegantly clever connections, so I could absolutely understand why this is eminently believable--especially in the context of AI and language models.
According to the descriptions from IBM [0]:
> Word embeddings capture the semantic relationships and contextual meanings of words based on their usage patterns in a given language corpus. Each word is represented as a fixed-sized dense vector of real numbers. It is the opposite of a sparse vector, such as one-hot encoding, which has many zero entries.
> The use of word embedding has significantly improved the performance of natural language processing (NLP) models by providing a more meaningful and efficient representation of words. These embeddings enable machines to understand and process language in a way that captures semantic nuances and contextual relationships, making them valuable for a wide range of applications, including sentiment analysis, machine translation and information retrieval.
> Popular word embedding models include Word2Vec, GloVe (Global Vectors for Word Representation), FastText and embeddings derived from transformer-based models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer).
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0. What is embedding? (https://www.ibm.com/think/topics/embedding)