🤖 AI Summary
This work addresses the limitations of existing large language models in tabular fact verification, which often rely on complex fine-tuning or task decomposition and suffer from insufficient interpretability and generalization. The authors propose MACE, a multi-agent framework comprising three zero-shot chain-of-thought agents—a planner, an executor, and a verifier—that collaboratively perform high-accuracy verification without any fine-tuning. By integrating planning, computation, and logical verification into a unified three-stage mechanism, MACE produces interpretable reasoning traces. Evaluated on four benchmark datasets, the method achieves performance on par with or close to state-of-the-art results: using only 27–92B parameter models, it attains 80–100% of the accuracy of a 235B-parameter model, substantially improving transparency, generalization, and memory efficiency.
📝 Abstract
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.