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New benchmark Intent2Tx evaluates LLMs for translating natural language to Ethereum transactions

Researchers have introduced Intent2Tx, a new benchmark designed to evaluate how well Large Language Models can translate natural language commands into Ethereum transactions. This benchmark includes over 31,000 instances derived from real-world Ethereum data, covering various Decentralized Finance (DeFi) operations. Evaluations of 16 leading LLMs showed that while models are improving, they still struggle with generalizing to new situations and complex multi-step transactions, often producing syntactically correct but functionally incorrect outputs. AI

IMPACT Establishes a new evaluation standard for LLM agents interacting with blockchain systems, highlighting current limitations in execution accuracy.

RANK_REASON Academic paper introducing a new benchmark for LLM capabilities.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New benchmark Intent2Tx evaluates LLMs for translating natural language to Ethereum transactions

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Academic paper introducing a new benchmark for LLM capabilities.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhuoran Pan, Yue Li, Zhi Guan, Jianbin Hu, Zhong Chen ·

    Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions

    arXiv:2604.27763v1 Announce Type: new Abstract: The emergence of Large Language Models (LLMs) offers a transformative interface for Web3, yet existing benchmarks fail to capture the complexity of translating high-level user intents into functionally correct, state-dependent on-ch…

  2. arXiv cs.AI TIER_1 English(EN) · Zhong Chen ·

    Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions

    The emergence of Large Language Models (LLMs) offers a transformative interface for Web3, yet existing benchmarks fail to capture the complexity of translating high-level user intents into functionally correct, state-dependent on-chain transactions. We present \textsc{Intent2Tx},…