# Dynamic Large Language Models on Blockchains

arXiv: https://arxiv.org/abs/2307.10549 · HTML: https://mlsanity.com/paper/2307.10549/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [Training Language Models with Language Feedback at Scale](https://mlsanity.com/paper/2303.16755/) (2023)
- [Side Window Filtering](https://mlsanity.com/paper/1905.07177/) (2019)
- [What do Large Language Models Learn about Scripts?](https://mlsanity.com/paper/2112.13834/) (2021)
- [Weighted Mean Curvature](https://mlsanity.com/paper/1903.07189/) (2019)
- [Fast and High-Quality Blind Multi-Spectral Image Pansharpening](https://mlsanity.com/paper/2103.09943/) (2021)
- [A Discrete Scheme for Computing Image's Weighted Gaussian Curvature](https://mlsanity.com/paper/2101.07927/) (2021)
- [Quarter Laplacian Filter for Edge Aware Image Processing](https://mlsanity.com/paper/2101.07933/) (2021)

## Built on by (most cited first, 5 of 5)

- [Blockchain for Large Language Model Security and Safety: A Holistic Survey](https://mlsanity.com/paper/2407.20181/) (2024)
- [Gradient Domain Diffusion Models for Image Synthesis](https://mlsanity.com/paper/2309.01875/) (2023)
- [TSSR: A Truncated and Signed Square Root Activation Function for Neural Networks](https://mlsanity.com/paper/2308.04832/) (2023)
- [Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration](https://mlsanity.com/paper/2412.13551/) (2024)
- [STL: A Signed and Truncated Logarithm Activation Function for Neural Networks](https://mlsanity.com/paper/2307.16389/) (2023)

## Abstract (arXiv)

Training and deploying the large language models requires a large mount of computational resource because the language models contain billions of parameters and the text has thousands of tokens. Another problem is that the large language models are static. They are fixed after the training process. To tackle these issues, in this paper, we propose to train and deploy the dynamic large language model on blockchains, which have high computation performance and are distributed across a network of computers. A blockchain is a secure, decentralized, and transparent system that allows for the creation of a tamper-proof ledger for transactions without the need for intermediaries. The dynamic large language models can continuously learn from the user input after the training process. Our method provides a new way to develop the large language models and also sheds a light on the next generation artificial intelligence systems.