@inproceedings{peng-etal-2026-txsum,
title = "{T}x{S}um: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding",
author = "Peng, Zifan and
Zheng, Jingyi and
Liu, Yule and
Jia, Huaiyu and
Ye, Qiming and
Liu, Jingyu and
Yang, Xufeng and
Li, Mingchen and
Gong, Qingyuan and
Wang, Xuechao and
He, Xinlei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.366/",
pages = "7660--7686",
ISBN = "979-8-89176-462-0",
abstract = "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.\par 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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<abstract>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.\par 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.</abstract>
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%0 Conference Proceedings
%T TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding
%A Peng, Zifan
%A Zheng, Jingyi
%A Liu, Yule
%A Jia, Huaiyu
%A Ye, Qiming
%A Liu, Jingyu
%A Yang, Xufeng
%A Li, Mingchen
%A Gong, Qingyuan
%A Wang, Xuechao
%A He, Xinlei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F peng-etal-2026-txsum
%X 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.\par 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.
%U https://aclanthology.org/2026.findings-emnlp.366/
%P 7660-7686
Markdown (Informal)
[TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding](https://aclanthology.org/2026.findings-emnlp.366/) (Peng et al., Findings 2026)
ACL
- Zifan Peng, Jingyi Zheng, Yule Liu, Huaiyu Jia, Qiming Ye, Jingyu Liu, Xufeng Yang, Mingchen Li, Qingyuan Gong, Xuechao Wang, and Xinlei He. 2026. TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 7660–7686, Budapest, Hungary. Association for Computational Linguistics.