Tag: token

Corpus, Ontology and LLM

Posted on by lechuck park

This diagram presents a unified framework consisting of three core structures, their interconnected relationships, and complementary utilization as the foundation for LLM advancement.

Three Core Structures

1. Corpus Structure

  • Token-based raw linguistic data
  • Provides statistical language patterns and usage frequency information

2. Ontology Structure

  • Systematically human-defined conceptual knowledge structure
  • Provides logical relationships and semantic hierarchies

3. LLM Structure

  • Neural network-based language processing model
  • Possesses pattern learning and generation capabilities

Interconnected Relationships and Interactions

  • Corpus → Vector Space: Numerical representation transformation of linguistic data
  • Ontology → Basic Concepts: Conceptual abstraction of structured knowledge
  • Vector Space ↔ Ontology: Mutual validation between statistical patterns and logical structures
  • Integrated Concepts → LLM: Multi-layered knowledge input

LLM Development Foundation through Complementary Relationships

Each structure compensates for the limitations of others:

  • Corpus’s statistical accuracy + Ontology’s logical consistency → Balanced knowledge foundation
  • Ontology’s explicit rules + LLM’s pattern learning → Flexible yet systematic reasoning
  • Corpus’s real-usage data + LLM’s generative capability → Natural and accurate language generation

Final Achievement

This triangular complementary structure overcomes the limitations of single approaches to achieve:

  • Error minimization
  • Human-centered reasoning capabilities
  • Intelligent and reliable response generation

This represents the core foundation for next-generation LLM development.

With Claude

Attention in a Transformer

Posted on by lechuck park

Attention Mechanism in Transformer Models

Overview

The attention mechanism in Transformer models is a revolutionary technology that has transformed the field of natural language processing. This technique allows each word (token) in a sentence to form direct relationships with all other words.

Working Principles

  1. Tokenization Stage: Input text is divided into individual tokens.
  2. Attention Application: Each token calculates its relevance to all other tokens.
  3. Mathematical Implementation:
    • Each token is converted into Query, Key, and Value vectors.
    • The relevance between a specific token (Query) and other tokens (Keys) is calculated.
    • Weights are applied to the Values based on the calculated relevance.
    • This is expressed as the ‘sum of Value * Weight’.

Multi-Head Attention

  • Definition: A method that calculates multiple attention vectors for a single token in parallel.
  • Characteristics: Each head (styles A, B, C) captures token relationships from different perspectives.
  • Advantage: Can simultaneously extract various information such as grammatical relationships and semantic associations.

Key Benefits

  1. Contextual Understanding: Enables understanding of word meanings based on context.
  2. Long-Distance Dependency Resolution: Can directly connect words that are far apart in a sentence.
  3. Parallel Processing: High computational efficiency due to simultaneous processing of all tokens.

Applications

Transformer-based models demonstrate exceptional performance in various natural language processing tasks including machine translation, text generation, and question answering. They form the foundation of modern AI models such as GPT and BERT.

With Claude