Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions
The advent of Large Language Models (LLMs) has the potential to revolutionize the interaction between users and the decentralized web, known as Web3. However, existing benchmarks in this space often fall short in accurately assessing the ability of these models to translate high-level user intents into functional, state-dependent transactions on the Ethereum blockchain. In response to this gap, researchers have introduced Intent2Tx, a comprehensive benchmark designed to evaluate the performance of LLMs in this critical area.
According to the newly released paper on arXiv (arXiv:2604.27763v1), Intent2Tx consists of a robust dataset of 29,921 single-step and 1,575 multi-step instances, all meticulously derived from 300 days of real-world Ethereum mainnet traces. This dataset is a significant advancement over previous benchmarks that relied primarily on synthetic instructions, thereby enhancing the relevance and applicability of the evaluations.
Key Features of Intent2Tx
- Real-World Data: The benchmark is grounded in actual protocol interactions, ensuring that the intents reflect genuine user behavior across 11 distinct categories, including various long-tail Decentralized Finance (DeFi) primitives.
- Execution-Aware Framework: Intent2Tx employs a sophisticated execution-aware framework that goes beyond superficial text matching. It incorporates differential state analysis on forked mainnet environments to rigorously evaluate the performance of LLMs.
- Extensive Evaluation: The researchers conducted an extensive evaluation of 16 state-of-the-art LLMs, uncovering strengths and weaknesses in their ability to handle intent translation tasks.
Findings from the Evaluation
The evaluation results indicate that while scaling and retrieval-augmentation techniques can improve logical consistency and parameter precision, current models still face significant challenges. Notably, they struggle with out-of-distribution generalization and the complexities involved in multi-step planning. This limitation is particularly crucial in the context of Web3, where user intents can often require intricate sequences of actions to be executed correctly.
One of the most striking findings from the study is the disconnect between syntactically valid outputs and their ability to achieve the intended state transitions on the Ethereum blockchain. This highlights a substantial gap in the “reasoning-to-execution” capabilities of existing LLMs and underscores the need for further advancements in this area.
Implications for Web3 Development
Intent2Tx is poised to serve as a foundational tool for the development of autonomous and reliable agents within intent-centric Web3 ecosystems. By providing a rigorous benchmarking framework, it encourages ongoing research and development aimed at enhancing the translation of natural language intents into executable blockchain transactions.
The researchers have made the code and data for Intent2Tx available for public access, enabling further exploration and innovation in this exciting field. For more details, interested parties can visit this link.
As the Web3 landscape continues to evolve, benchmarks like Intent2Tx will be critical in shaping the capabilities of AI models and ensuring that they can meet the complex demands of users in decentralized environments.
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