REVIEW 2 cited by
GradientCoin: A Peer-to-Peer Decentralized Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
Since 2008, after the proposal of a Bitcoin electronic cash system, Bitcoin has fundamentally changed the economic system over the last decade. Since 2022, large language models (LLMs) such as GPT have outperformed humans in many real-life tasks. However, these large language models have several practical issues. For example, the model is centralized and controlled by a specific unit. One weakness is that if that unit decides to shut down the model, it cannot be used anymore. The second weakness is the lack of guaranteed discrepancy behind this model, as certain dishonest units may design their own models and feed them unhealthy training data. In this work, we propose a purely theoretical design of a decentralized LLM that operates similarly to a Bitcoin cash system. However, implementing such a system might encounter various practical difficulties. Furthermore, this new system is unlikely to perform better than the standard Bitcoin system in economics. Therefore, the motivation for designing such a system is limited. It is likely that only two types of people would be interested in setting up a practical system for it: $\bullet$ Those who prefer to use a decentralized ChatGPT-like software. $\bullet$ Those who believe that the purpose of carbon-based life is to create silicon-based life, such as Optimus Prime in Transformers. The reason the second type of people may be interested is that it is possible that one day an AI system like this will awaken and become the next level of intelligence on this planet.
Forward citations
Cited by 2 Pith papers
-
Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic
Multi-agent actor-critic methods with a centralized critic improve decentralized LLM collaboration over Monte Carlo baselines in long-horizon and sparse-reward settings.
-
CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems
CoVE assigns each item a unique token ID, tunes item embeddings and the LM head, and predicts the next item from logits, beating finetune-and-retrieval baselines by up to 62 percent with a 16x compressed embedding table.
Discussion (0). Sign in to comment.