Summary of Proof Of Quality: a Costless Paradigm For Trustless Generative Ai Model Inference on Blockchains, by Zhenjie Zhang et al.
Proof of Quality: A Costless Paradigm for Trustless Generative AI Model Inference on Blockchains
by Zhenjie Zhang, Yuyang Rao, Hao Xiao, Xiaokui Xiao, Yin Yang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel inference paradigm called proof of quality (PoQ) is proposed to enable the deployment of large generative models on blockchain architecture. Unlike traditional approaches, PoQ focuses on the outcome quality of model inference rather than validating inference procedures. The protocol uses lightweight BERT-based cross-encoders as the underlying quality evaluation model and is tailored for popular open-source models such as Llama 3 and Mixtral. The authors demonstrate that their protocol is robust against adversarial participants, with minimal computational overhead for validating quality evaluations. Preliminary simulation results show that PoQ consensus is generated in milliseconds, outperforming existing schemes by a factor of 1,000. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new way to use big language models on the blockchain. This allows people to trust that the model’s answers are correct and trustworthy. The team creates a new system called “proof of quality” (PoQ) that checks the accuracy of the model’s responses. They use a special type of computer model to do this, which is fast and efficient. This means that people can quickly verify that the model’s answers are accurate, even on big models like Llama 3 and Mixtral. |
Keywords
» Artificial intelligence » Bert » Inference » Llama
