用互动平台分析大模型在政治投票中的性别与立场偏见
ParlAI Vote: A Web Platform for Analyzing Gender and Political Bias in Large Language Models
- 整合欧洲议会辩论、投票和人口数据，统一测试大模型预测能力
- 发现顶尖大模型在性别分类和投票预测中存在系统性偏差
- 可视化模型推理过程，适合研究者、教育者和公众使用
我们推出 ParlAI Vote，一个交互式网络平台，用于探索欧洲议会辩论与投票记录，并测试大语言模型在投票预测与偏见分析中的表现。该平台关联议题、发言内容与投票结果，包含性别、年龄、国家、政党等丰富的人口统计信息。用户可浏览辩论、查看发言、对比真实投票与大模型预测结果，并按人口群体查看错误分布。平台展示 EuroParlVote 基准及其核心任务——性别分类与投票预测，揭示当前前沿大模型存在系统性性能偏差。它将数据、模型与可视化分析集成于单一界面，降低复现研究、审计行为与开展反事实分析的门槛。平台还呈现模型推理过程，帮助理解错误成因及模型依赖线索。适用于研究、教学与公众参与立法决策，清晰展现当前大模型在政治分析中的优势与局限。
原文摘要 · Abstract (English)
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.
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