Intent2Tx shows that LLMs often generate syntactically valid but functionally incorrect Ethereum transactions, especially on multi-step and out-of-distribution intents, despite gains from scaling and retrieval augmentation.
Modeling and understanding ethereum transaction records via a complex network approach.IEEE Transactions on Circuits and Systems II: Express Briefs 67, 11 (2020), 2737–2741
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Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions
Intent2Tx shows that LLMs often generate syntactically valid but functionally incorrect Ethereum transactions, especially on multi-step and out-of-distribution intents, despite gains from scaling and retrieval augmentation.