Unveiling the Magic: Compressing Verbosity for Large Language Models

The Importance of Large Language Models

The rise of large language models has revolutionized the world of natural language processing (NLP) and AI. Models have achieved outstanding performance in tasks from text generation to sentiment analysis. However, both the input and output of these models are subject to token limits. Given that only so many tokens can be processed, and that each token is roughly equivalent to a modest-length word, there exists a limit on how much information external to the language model can be analyzed. 

To help target large language models on the relevant portions of a document embedding models can be employed. 

Embedding models make it possible for sophisticated language models to function efficiently. At their core, language embedding utilizes feature representation that maps high-dimensional, sparse data, such as words or sentences, into dense, continuous vector spaces of fixed dimensions. Languages employ extensive vocabularies, often in the order of hundreds of thousands or even millions of unique words. For computers, each word can be represented by a one-hot vector, a binary vector with a single ‘1’ and the rest ‘0’s. 

Embedding models convert these high-dimensional one-hot vectors into lower-dimensional dense embeddings. This dimensionality reduction significantly reduces the memory footprint required to store the vocabulary, making the training and deployment of large language models feasible. To maintain semantic relationships words with similar meanings are mapped closer together in the embedding space, while dissimilar words are farther apart.

Chunks of large documents can be encoded with embedding models and still retain significant structure even if all the nuance of a large language model is not maintained. By applying the same model to a question about the larger document, these vectors (the set of vectors from the document and the vector from the question) can be compared to find close matches. The text that produced this similar vector can then be passed into a model with the question rather than the entire document. This will enable one to leverage the full power of the language model without needing to process the entire document, a task that could not be done due to token limits.

Embedding models play a pivotal role in enabling large language models to handle vast amounts of textual data efficiently. By reducing dimensionality, compressing semantic information, and enabling more efficient input, embedding models empower sophisticated language models to process, generate, and comprehend text at an unprecedented scale. As LLMs are integrated into more technologies, embedding models will remain crucial to making use of this technology.