---
title: "A Comprehensive Overview of Large Language — Naveed 2023"
description: "The volume of LLM research has grown so quickly that it is hard to see the field as a whole."
authors: ["Humza Naveed", "Asad Ullah Khan", "Shi Qiu", "Muhammad Saqib", "Saeed Anwar", "Muhammad Usman", "Naveed Akhtar", "Nick Barnes", "Ajmal Mian"]
year: 2023
venue: "arXiv:2307.06435"
source: "https://arxiv.org/abs/2307.06435"
doi: "10.48550/arxiv.2307.06435"
arxiv: "2307.06435"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/"
---
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# A Comprehensive Overview of Large Language Models

**Authors:** Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, Ajmal Mian · **Year:** 2023 · **Venue:** arXiv:2307.06435 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2307.06435) · [PDF](https://arxiv.org/pdf/2307.06435) · [DOI 10.48550/arxiv.2307.06435](https://doi.org/10.48550/arxiv.2307.06435) · [arXiv 2307.06435](https://arxiv.org/abs/2307.06435) · [OpenAlex](https://openalex.org/W4411403346)

**Citations:** 1097 citations (openalex-snapshot · 2026-10-14) · [189](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The volume of LLM research has grown so quickly that it is hard to see the field as a whole. This survey addresses that by organising the literature into one self-contained reference. It covers pre-trained models of roughly 10B parameters and up. The authors first explain core building blocks: tokenization, positional encodings, attention, activation functions and normalization. They then give detailed per-model summaries of architecture and training setups. Later sections cover fine-tuning and alignment, multi-modal and retrieval-augmented LLMs, LLM-based agents, efficiency techniques, datasets, benchmarks, applications and open problems. The paper introduces no new model or corpus. Its contribution is synthesis: it compares zero-shot and few-shot behaviour, measures the effect of instruction tuning, and contrasts encoder, decoder and encoder-decoder designs. For teams building or adapting language models, including for lower-resource languages such as Ukrainian, it works as a map of design choices and their trade-offs. The arXiv version used here is v10 (October 2024).

## Key points
- **Seven-branch taxonomy:** the field is split into pre-training, fine-tuning, efficiency, inference, evaluation, applications and challenges.
- **Model timeline:** releases run from T5/mT5 and GPT-3 through PaLM, LLaMA, BLOOM and Mixtral to GPT-4o and OpenAI o1. Each model is marked as pre-trained or instruction-tuned and as open or closed. The trend favours instruction-tuned, open-source models.
- **Background components:**
  - Tokenizers: wordpiece, BPE and unigramLM.
  - Relative positional schemes: Alibi and RoPE.
  - Activations: GLU variants such as ReGLU, GEGLU and SwiGLU.
  - Normalization: RMSNorm, pre-norm and DeepNorm, each with its stability trade-offs.
- **Efficiency methods:** parameter-efficient tuning, pruning, quantization, knowledge distillation and context-length interpolation.
- **Open challenges:**
  - Hallucination, split into input-, context- and fact-conflicting types.
  - Adversarial robustness and interpretability.
  - Privacy leakage through memorised training text.
  - Real-time and edge deployment, long-range context and hardware limits.
  - Regulation, with auditing proposed as a governance tool.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) · [AI & machine learning](https://md.research.elamur.ai/free/en/shelves/ai/)

## Cited by

Cited by

189 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 185 not yet.

- [GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning](https://md.research.elamur.ai/free/en/papers/chouikhi-2024-gemmar-enhancing-llms-through-arabic/)
- [Security and Privacy Challenges of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/das-2024-security-privacy-challenges-large-language/)
- [基于大语言模型智能体的代码生成综述](https://md.research.elamur.ai/free/en/papers/dong-2025-code-generation-llm/)
- [Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{naveed2023comprehensive,
  title = {{A Comprehensive Overview of Large Language Models}},
  author = {Naveed, Humza and Khan, Asad Ullah and Qiu, Shi and Saqib, Muhammad and Anwar, Saeed and Usman, Muhammad and Akhtar, Naveed and Barnes, Nick and Mian, Ajmal},
  year = {2023},
  doi = {10.48550/arxiv.2307.06435},
  eprint = {2307.06435},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2307.06435}
}
```

- [← previous rev. of S. BETTEGA, L. D’ANNA, "Gender and Number Agreement in Arabic", Brill, 2023](https://md.research.elamur.ai/free/en/papers/petrantoni-2023-rev-s-bettega-l-danna/)
- [next → A fine-grained comparison of pragmatic language understanding in humans and language models](https://md.research.elamur.ai/free/en/papers/hu-2023-fine-grained-comparison-pragmatic-language/)
- [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/)

## References

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