Circle and Berkeley Utilize AI for Blockchain Transactions with TXT2TXN
Timothy Morano Aug 08, 2024 15:58
Circle and Blockchain at Berkeley introduce TXT2TXN, an AI-driven tool using Large Language Models to simplify blockchain transactions through intent-based applications.
Circle and Blockchain at Berkeley have unveiled TXT2TXN, an innovative open-source web application that leverages Large Language Models (LLMs) to streamline blockchain transactions by interpreting user intents from natural language inputs, according to circle.com.
Introduction to TXT2TXN
TXT2TXN, which stands for text-to-transaction, aims to simplify the user experience in crypto applications by using AI to translate freeform English text into actionable blockchain transactions. This tool allows users to specify their desired actions in natural language, which the LLM then interprets and converts into signed intents for on-chain execution.
Understanding User Intents
The concept of intents refers to a user's expression of their desired outcome without detailing the specific steps needed to achieve it. Most existing blockchain applications operate on a transaction-based architecture, where users must specify each step of the transaction process. In contrast, an intent-based architecture allows users to define what they want to achieve, leaving the application to determine how to accomplish it.
Circle and Berkeley's research highlights that intents can simplify complex interactions, making blockchain technology more accessible. For instance, instead of manually setting up a token swap, a user can simply state their intention, and the system will handle the intricate details.
LLMs in Action
Large Language Models play a crucial role in this new architecture. By interpreting natural language inputs, LLMs can classify user intents and convert them into executable transactions. This reduces the complexity of user interfaces, allowing users to focus on their desired outcomes rather than the technical steps required.
For example, a user might input a command like “send 1 USDC on Ethereum to kaili.eth,” and the LLM will classify this as a transfer intent. The application then processes this intent and generates a transaction payload to be executed on the blockchain.
The Prototype and Its Functionality
The TXT2TXN prototype uses OpenAI’s GPT-3.5 Turbo to interpret user intents in two stages. First, it classifies the input into a specific intent type, such as transfer or swap. Second, it fills in the necessary details to create a transaction payload or a signed order, which the user can then execute.
Circle and Berkeley have implemented a simple frontend where users can input their desired actions in a textbox. The backend processes these inputs, leveraging the LLM to determine the appropriate intent and generate the necessary transaction details.
Accuracy and Future Work
Accuracy is a critical concern when using LLMs for blockchain transactions, given the irreversible nature of most blockchain operations. Preliminary tests with a small array of prompts have shown promising results, but further research is needed to ensure reliability and minimize errors.
Future developments for TXT2TXN include expanding the range of supported intent types, enhancing accuracy through advanced learning techniques, and integrating more stateful features like personal address books. These improvements aim to make blockchain technology even more accessible and user-friendly.
Conclusion
TXT2TXN represents a significant step forward in the integration of AI and blockchain technology. By simplifying user interactions and leveraging the power of LLMs, Circle and Berkeley are paving the way for more intuitive and efficient crypto applications. As this technology evolves, it promises to make blockchain transactions more accessible to a broader audience.
Acknowledgements
This project was a collaborative effort between Blockchain at Berkeley and Circle Research. The team included Niall Mandal, Teo Honda-Scully, Daniel Gushchyan, Naman Kapasi, Tanay Appannagari, Adrian Kwan from Berkeley, and Alex Kroeger and Kaili Wang from Circle.
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