Web3 prediction markets, exemplified by Polymarket, have gained prominence for leveraging collective intelligence to forecast a wide range of social, political, and sports events. However, among the thousands of prediction market events, consensus disputes still arise due to imperfections in market mechanisms. On Polymarket alone, the trading volume involving disputed events has reached $972,370,804.71, underscoring the critical need for objective and efficient dispute resolution. In this study, we introduce large language models (LLMs) to: (1) evaluate whether web-enabled LLMs can reproduce the decision quality of UMA's on-chain voting process once a dispute has been raised, and (2) predict, based on event rules, which market events are likely to face future disputes before they occur. Our findings show that LLMs are unable to reliably predict which events will become disputed in advance; however, once a dispute is initiated, web-enabled LLMs achieve 89.58% agreement with UMA's final resolutions and demonstrate strong stability.

翻译：Web3预测市场（以Polymarket为代表）因利用群体智慧预测广泛的社会、政治及体育事件而备受关注。然而，在成千上万的预测市场事件中，由于市场机制的不完善，仍会出现共识争议。仅Polymarket平台，涉及争议事件的交易额已达972,370,804.71美元，这凸显了客观高效争议解决机制的迫切需求。本研究引入大型语言模型（LLM）以：（1）评估联网LLM能否在争议发生后复现UMA链上投票过程的决策质量；（2）基于事件规则，预测哪些市场事件可能在发生前面临未来争议。研究结果表明：LLM无法可靠地提前预测哪些事件将成为争议焦点；但在争议启动后，联网LLM与UMA最终裁决的一致性达到89.58%，并展现出强大的稳定性。