让普通用户看懂以太坊交易细节，防止盲签风险。
TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding
- 基于用户调研构建微观代币流动解释框架
- 复杂交易理解准确率提升至76.5%，恶意交易识别率达88%
- 适合普通用户、钱包开发者和DeFi安全研究者
理解以太坊交易的经济意图对用户安全至关重要，但现有工具仅展示原始链上数据或表面意图，导致普遍出现“盲签”现象（在未理解情况下批准交易）。通过对16名Web3用户的访谈发现，有效解释应具备结构化、风险意识，并在代币流动层面具备语义基础。受此启发，我们提出TxSum——一种面向DeFi交易解释的新领域接地自然语言处理任务，并构建包含187笔复杂以太坊交易、2,375条代币流动标注及交易级摘要的数据集。我们进一步提出MATEX，一个基于多智能体的高风险交易解释框架。它在不确定时选择性检索外部知识，并通过原始交易痕迹审计解释内容，以提升代币流动层面的事实一致性。MATEX在整体解释质量上表现最佳，尤其在微观事实性和意图准确性方面。相比最强基线，其使复杂交易理解率从52.9%提升至76.5%，恶意交易拒绝率从36.0%提升至88.0%，同时保持对良性交易的低误拒率。
原文摘要 · Abstract (English)
Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data or surface-level intent, leading to widespread ``blind signing'' (approving transactions without understanding them). Through interviews with 16 Web3 users, we find that effective explanations should be structured, risk-aware, and grounded at the token-flow level. Motivated by these findings, we formulate TxSum, a new domain-grounded NLP task for DeFi transaction explanation, and construct a dataset of 187 complex Ethereum transactions with 2,375 token-flow annotations and transaction-level summaries. We further introduce MATEX, a grounded multi-agent framework for high-stakes transaction explanation. It selectively retrieves external knowledge under uncertainty and audits explanations against raw traces to improve token-flow-level factual consistency. MATEX achieves the strongest overall explanation quality, especially on micro-level factuality and intent quality. It improves user comprehension on complex transactions from 52.9% to 76.5% over the strongest baseline and raises malicious-transaction rejection from 36.0% to 88.0%, while maintaining a low false-rejection rate on benign transactions.
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