Ways to Understand the Operation of LLMs Intuitively.
Embeddings -- (Latent) Embedded Space of (Semantic and) Conceptual Islands/Clusters/Geological features and the game of hopskotch that unites them.
Large Language Models are currently all the rage and they are quite helpful in getting research accomplished. How do they work?
Terms are grouped according to their similarity, forming clustered islands of concepts and semantic comprehensions.
These clusters are then situated within an atmosphere, creating what is known as an embedding space.
To unleash and fully utilize the power of the LLM, we engage in a statistical game of hopscotch, swiftly moving from island-to-island, grouping-to-grouping.
Introduction
Large Language Models (LLMs) like GPT and BERT have revolutionized how machines understand human language. By breaking down the operation of these models into intuitive concepts, we can better appreciate their capabilities and applications.
Conceptual Foundations
Latent Embeddings as Conceptual Groupings: Latent embeddings simplify the complexity of language by grouping similar words or phrases into conceptual clusters. These clusters form conceptual islands in a vast sea of data, making it easier for the model to process and understand language by reducing it to manageable, related groups.
Token Complexity and Conceptual Reasoning: Instead of dealing with the overwhelming variety of individual words (tokens), LLMs manage complexity by focusing on higher-level concepts. This approach groups tokens that share meanings, contexts, or functions, thereby creating a simplified conceptual map.
Visual Metaphors and Analogies
Islands of Concepts: Visualize each concept as an island. These islands are not random; they are formed by the gravitational pull of semantic similarity, where closer islands are more closely related in meaning.
The Atmosphere of Embedding Space: The embedding space where these islands exist can be thought of as the atmosphere encompassing them. It's a dynamic, multidimensional space where each dimension can represent linguistic features like tense, sentiment, or formality.
Geological Features: These islands might have various features—peaks representing common or core terms, valleys for rarer synonyms, and plateaus for terms used in similar contexts. Understanding these features helps in navigating the language model's understanding of text.
Connecting Concepts: The Game of Hopscotch
In this metaphorical landscape, making connections between concepts (islands) involves a game of hopscotch. The model 'hops' from one island to another using links formed by contextual relationships or transitional phrases, effectively traversing the embedding space to generate coherent and contextually appropriate responses.
Conclusion
Understanding LLMs through these intuitive visual metaphors simplifies their complex operations, making it clear how these models manage to interpret and generate human-like text. By viewing concepts as islands in an embedding space, we can appreciate the intricate yet orderly nature of language processing in AI.