Building a LLM from Scratch: Replacing Transformers with Hyperdimensional Computing and Neuro-Symbolic AI Enhanced with Knowledge Distillation

Introduction

In the crazy fast changing world of artificial intelligence (AI), large language models (LLMs) have emerged as powerful tools for natural language understanding and generation. This blog post presents a comprehensive approach to building a large language model from scratch, focusing on replacing traditional transformer architectures with innovative techniques: Hyperdimensional Computing (HDC) and Neuro-Symbolic AI (NSAI).

Additionally, we will explore the process of knowledge distillation using pre-trained models, enhancing our model’s capabilities while reducing complexity.

Overview

What You Will Learn

1. Understanding Hyperdimensional Computing (HDC): A mathematical framework for efficiently representing and manipulating data in high-dimensional spaces.
2. Exploring Neuro-Symbolic AI (NSAI): A hybrid approach combining neural networks with symbolic reasoning to enhance model interpretability and decision-making.
3. Utilizing Knowledge Distillation: Extracting knowledge from a pre-trained model to improve the performance of a new model while maintaining efficiency.
4. Building and Training the Model: A step-by-step guide to constructing a neural network that incorporates HDC and NSAI.
5. Performance Evaluation: Implementing cross-validation and evaluating the model’s effectiveness through various metrics.

Mathematics of Hyperdimensional Computing

1. Vector Representation

At the heart of Hyperdimensional Computing is the concept of high-dimensional vectors (hypervectors). Each token in our dataset will be represented as a hypervector in a space with thousands of dimensions. The idea is to leverage the unique properties of high-dimensional spaces to capture the semantics of the data more effectively.

Each dimension of the hypervector can represent a different feature of the token, and the use of binary values (-1 or 1)…