LLM Architectures
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SkillfulAI_Docs_v1.0
Last updated
LLM is a Large Language Model. Yes. It’s Large. Like… really really large.
And It’s a “Language Model” which is a kind of brainiac for words, sentences, and all things linguistic.
An LLM is basically two files - the parameters (essentially, the model's knowledge, stored in a huge file) and the code that brings this knowledge to life.
The key part is the parameter file. It is a giant list of numbers that represent the "weights" or "strengths" of the connections in the language model's neural network - a computing system inspired by the human brain.
These weights allow the network to understand and generate human-like text. In other words, this file contains numbers representing how likely each word is to follow other words. Words with higher numbers are more probable predictions, allowing the model to generate sensible continuations.
Think of teaching a child to read. You start with simple sentences like "The cat sat on the mat". As the child sees more sentences, they recognize patterns, such as "cat" often comes before "sat." This process is similar to how a large language model learns.
A weight might show how "cat" is related to "sat." The model adjusts these weights to learn which word combinations make sense. When you give the model a prompt, it uses this network of weights to generate a correct response, similar to how we use our language knowledge.
Below are some of the LLM architecture that Skillful is using in their products
GPT (Generative Pretrained Transformer) models are based on the transformer architecture, primarily designed for generating human-like text.
Pros: Exceptional at generating coherent and contextually relevant text; highly versatile in various language tasks. Enables faster training and inference by processing entire sequences simultaneously.
Cons: Requires extensive data and computational resources for training; sometimes generates plausible but factually incorrect information.
These models are the core of Skillful AI advanced virtual assistants leveraging their ability to generate human-like responses and maintain context over extended interactions. This can enhance user-specific memory features and provide personalized experiences.
Autoencoders are unsupervised learning models used for data encoding and decoding, often applied in dimensionality reduction and feature learning.
Architecture Contrast: Unlike GPT, which predicts the next token in a sequence, autoencoders focus on learning a compressed representation of input data.
Pros: Excellent at learning efficient representations; useful in noise reduction and data denoising. Effective for Summarization.
Cons: Less effective in handling sequential data like natural language; struggle with maintaining context over longer texts compared to transformer models.
Skillful AI's memory feature uses this for efficient data storage and recall, optimizing the AI's ability to access and utilize user-specific data for more tailored interactions and responses.
3. Sequence-to-Sequence (Seq2Seq) Models: An Alternative to GPT
Seq2Seq models are designed for transforming a sequence from one domain to another, commonly used in machine translation and speech recognition.
Architecture Contrast: Seq2Seq models typically combine two recurrent neural networks (RNNs)—an encoder and a decoder—providing a different approach compared to GPT's transformer-based single model strategy. Unlike GPT's focus on token prediction, Seq2Seq models aim to change entire sequences, maintaining context and meaning across domains.
Pros: Effective in tasks involving two different sequential domains, like translating between languages; better at handling context in conversation.
Cons: Requires paired sequence data for training; can struggle with very long sequences due to RNN limitations, Requires careful training to ensure quality outputs,
Complement to LLMs: In tasks like translation or summarization, Seq2Seq models can offer more specialized performance compared to general-purpose LLMs like GPT.
By using variety of large language model (LLM) architectures, Skillful AI can deliver more personalized efficient solutions to meet a wide range of user needs.
