Internet-scale Training Corpus

The large corpus of human-generated text on which LLMs are trained, which provisions character archetypes and narrative structures

Neighborhood — ranked by edge-count

concept

Large Language Models (LLMs)
associated_with
Transformer-based models like GPT-4, LaMDA, PaLM; assessed for GWT indicators.

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

as the subject model scales, how does the ideal expansion factor and required training data for dictionary learning change?question0.714
Scaling laws for dictionary learning are unknown and needed to assess feasibility on frontier models
Deep Reinforcement Learningmethod0.714
AI training method inspired by behaviorism, used for autonomous cars and drones; cited as bioinspired success
language models recapitulate cyclic structure of human concepts from pretraining datahypothesis0.713
Explanation for why manifold geometry emerges: implicit structure in training data (co-occurrence patterns) shapes internal representations.
Conceptual Scalingmethod0.712
Interpretive process for transforming many-valued contexts into formal contexts via scale attributes.
Training corpora may produce civilizational psychologies; cross-cultural mind is embedded in model training data.claim0.710
scaleconcept0.709
A formal context with a suggestive interpretation used in conceptual scaling.
Ouyang et al. 2022: Training language models to follow instructions with human feedbackconcept0.709
RLHF paper cited as a major fine-tuning technique used in commercial dialogue agents
Human Scaleconcept0.708
The quality of spatial dimensions that feel comfortable and nurturing to human beings.