We present ParlAI Vote, an interactive web platform for exploring European Parliament debates and votes, and for testing LLMs on vote prediction and bias analysis. This web system connects debate topics, speeches, and roll-call outcomes, and includes rich demographic data such as gender, age, country, and political group. Users can browse debates, inspect linked speeches, compare real voting outcomes with predictions from frontier LLMs, and view error breakdowns by demographic group. Visualizing the EuroParlVote benchmark and its core tasks of gender classification and vote prediction, ParlAI Vote highlights systematic performance bias in state-of-the-art LLMs. It unifies data, models, and visual analytics in a single interface, lowering the barrier for reproducing findings, auditing behavior, and running counterfactual scenarios. This web platform also shows model reasoning, helping users see why errors occur and what cues the models rely on. It supports research, education, and public engagement with legislative decision-making, while making clear both the strengths and the limitations of current LLMs in political analysis.

翻译：我们推出ParlAI Vote，这是一个交互式网络平台，用于探索欧洲议会辩论与投票，并测试大型语言模型在投票预测与偏见分析上的表现。该网络系统将辩论主题、演讲内容及唱名表决结果相互关联，并整合了丰富的群体特征数据，如性别、年龄、国家及政治团体。用户可浏览辩论内容、查看关联演讲、将真实投票结果与前沿大型语言模型的预测进行对比，并按群体特征查看误差分解。通过可视化EuroParlVote基准及其性别分类与投票预测核心任务，ParlAI Vote突显了当前最先进大型语言模型中存在的系统性性能偏差。它将数据、模型与可视化分析统一于单一界面，降低了复现研究结果、审计模型行为及运行反事实场景的门槛。该平台还展示了模型的推理过程，帮助用户理解错误产生的原因及模型所依赖的线索。它支持立法决策相关的研究、教育与公众参与，同时清晰揭示了当前大型语言模型在政治分析中的优势与局限。