@inproceedings{saha-etal-2026-multi,
title = "A Multi-Agent Approach for Claim Verification from Tabular Data Documents",
author = "Saha, Rudra Ranajee and
Lakshmanan, Laks V. S. and
Ng, Raymond T.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2117/",
pages = "45135--45154",
ISBN = "979-8-89176-462-0",
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 fine-tuning, 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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<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 fine-tuning, 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.</abstract>
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%0 Conference Proceedings
%T A Multi-Agent Approach for Claim Verification from Tabular Data Documents
%A Saha, Rudra Ranajee
%A Lakshmanan, Laks V. S.
%A Ng, Raymond T.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F saha-etal-2026-multi
%X 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 fine-tuning, 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.
%U https://aclanthology.org/2026.findings-emnlp.2117/
%P 45135-45154
Markdown (Informal)
[A Multi-Agent Approach for Claim Verification from Tabular Data Documents](https://aclanthology.org/2026.findings-emnlp.2117/) (Saha et al., Findings 2026)
ACL