On Large Language Models and Architectures. Q&A with Neil Kanungo

Q1. What is Large Language Model Architecture?

Large Language Model Architecture refers to the design and structure of models that specialize in understanding and generating text. The most prevalent architecture is Transformer-Based, which employs deep neural networks to encode textual data into vector embeddings. These embeddings are then processed through multiple layers of transformers, each adding a layer of contextual understanding. For instance, the first transformer layer might identify the grammatical roles of words, while subsequent layers could discern the specific meaning of a word like “bank” in a given context. This multi-layered approach allows the model to build a rich, contextual understanding of the text. The architecture is designed to handle a high dimensionality of data.

Q2. What are foundation models in AI?

Foundation models in AI are large-scale, adaptable models trained on massive amounts of unlabelled data. They serve as a “paradigm for building AI systems,” capable of a wide variety of tasks, from text translation to image recognition. Unlike narrow AI models focused on a single task, foundation models can transfer knowledge from one domain to another. They are a form of generative AI, designed to produce a wide range of outputs based on one or more inputs. These models are reshaping enterprise AI but come with their own set of challenges, such as biases and security risks.

Q3. What are Large Language Models (LLMs)? Are they the same as foundation models?

LLMs are a subset of foundation models, specialized in understanding and generating human-like text. They come in various architectures, including Autoencoder-Based, Sequence-to-Sequence, and Transformer-Based models. Like foundation models, LLMs are trained on large datasets and can be fine-tuned for specific applications. However, their primary focus is on text-based tasks. For example, ChatGPT and Anthropic’s Claude are Transformer-Based LLMs that are pre-trained on massive text corpora and fine-tuned for chat applications.

Q4. What is the relationship between Large Language Models and generative AI?

LLMs are a specific form of generative AI, designed to produce human-like text based on the context provided. They employ a multi-layered architecture of transformers to encode, manipulate, and decode data into meaningful text. This generative capability is fine-tuned for various tasks, such as Text Summarization, Language Generation, or Question-Answering. The architecture allows for the incorporation of additional data sources, like vector databases, to augment the model’s knowledge base, thereby enhancing its generative capabilities.

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Neil KanungoVP of Product Led Growth, KX

Sponsored by KX

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