Atomic Reasoning for Scientific Table Claim Verification

• URL: http://arxiv.org/abs/2506.06972v1
• Date: Sun, 08 Jun 2025 02:46:22 GMT
• Title: Atomic Reasoning for Scientific Table Claim Verification
• Authors: Yuji Zhang, Qingyun Wang, Cheng Qian, Jiateng Liu, Chenkai Sun, Denghui Zhang, Tarek Abdelzaher, Chengxiang Zhai, Preslav Nakov, Heng Ji,
• Abstract summary: Non-experts are susceptible to misleading claims based on scientific tables due to their high information density and perceived credibility.<n>Existing table claim verification models, including state-of-the-art large language models (LLMs), often struggle with precise fine-grained reasoning.<n>Inspired by Cognitive Load Theory, we propose that enhancing a model's ability to interpret table-based claims involves reducing cognitive load.
• Score: 83.14588611859826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scientific texts often convey authority due to their technical language and complex data. However, this complexity can sometimes lead to the spread of misinformation. Non-experts are particularly susceptible to misleading claims based on scientific tables due to their high information density and perceived credibility. Existing table claim verification models, including state-of-the-art large language models (LLMs), often struggle with precise fine-grained reasoning, resulting in errors and a lack of precision in verifying scientific claims. Inspired by Cognitive Load Theory, we propose that enhancing a model's ability to interpret table-based claims involves reducing cognitive load by developing modular, reusable reasoning components (i.e., atomic skills). We introduce a skill-chaining schema that dynamically composes these skills to facilitate more accurate and generalizable reasoning with a reduced cognitive load. To evaluate this, we create SciAtomicBench, a cross-domain benchmark with fine-grained reasoning annotations. With only 350 fine-tuning examples, our model trained by atomic reasoning outperforms GPT-4o's chain-of-thought method, achieving state-of-the-art results with far less training data.

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