用三个智能体协作验证表格数据中的声明，既快又透明。
A Multi-Agent Approach for Claim Verification from Tabular Data Documents
- 三个专用智能体分工：规划、执行、验证，零样本推理
- 在多个数据集上达顶尖性能，模型规模小80%以上
- 推理过程可解释，适合需要可信AI的场景
我们提出一种用于表格数据声明验证的新方法。现有基于大语言模型的方法要么需复杂微调，要么将验证拆解为子任务，常缺乏全面解释且泛化能力弱。为此，我们设计多智能体框架MACE，包含规划者、执行者和验证者三个专门代理。各代理采用零样本思维链机制完成任务，无需复杂微调。MACE生成可解释的验证轨迹：规划者输出明确推理策略，执行者提供详细计算步骤，验证者校验逻辑正确性。实验表明，MACE在两个数据集上达到当前最优（SOTA）表现，在另两个数据集上与最佳模型性能相当，且仅需27–92B参数，远低于235B的基准模型，性能保留率达80%–100%。该框架兼具优异性能、内存效率与透明推理，展现出显著优势。
原文摘要 · Abstract (English)
We present a novel approach for claim verification from tabular data documents. Recent LLM-based approaches either employ complex pretraining/fine-tuning or decompose verification into subtasks, often lacking comprehensive explanations and generalizability. To address these limitations, we propose a Multi-Agentic framework for Claim verification (MACE) consisting of three specialized agents: Planner, Executor, and Verifier. Instead of elaborate finetuning, each agent employs a zero-shot Chain-of-Thought setup to perform its tasks. MACE produces interpretable verification traces, with the Planner generating explicit reasoning strategies, the Executor providing detailed computation steps, and the Verifier validating the logic. Experiments demonstrate that MACE achieves state-of-the-art (SOTA) performance on two datasets and performs on par with the best models on two others, while achieving 80--100\% of best performance with substantially smaller models: 27--92B parameters versus 235B. This combination of competitive performance, memory efficiency, and transparent reasoning highlights our framework's effectiveness.
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