From GPT to LLaMA: Tracing the Growth of Large Language Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Large Language Models (LLMs) have transformed natural language processing by scaling model parameters to unprecedented levels. This review traces the historical progression of LLM parameter sizes, from early pre-trained models with millions of parameters to today’s multi-billion and even trillion-parameter systems. We examine key breakthroughs in scaling (e.g., the GPT series, PaLM, LLaMA), highlighting how increasing model size has led to emergent capabilities in language understanding and generation. We also discuss the engineering innovations, such as Transformer architectures and mixture-of-experts, that enabled these leaps in scale. A comparative analysis is provided, including a table and trend figure, to illustrate growth in parameter counts over time and across model families. We further explore the implications of model size on performance, emergent behaviors, and computational cost, noting scaling laws and diminishing returns. Finally, we discuss future directions, arguing that while scaling has driven progress, challenges in efficiency, alignment, and data quality will shape the next phase of LLM development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it