Atomic Reasoning for Scientific Table Claim Verification
Yuji Zhang, Qingyun Wang, Cheng Qian, Jiateng Liu, Chenkai Sun, Denghui Zhang, Tarek Abdelzaher, Chengxiang Zhai, Preslav Nakov, Heng Ji

TL;DR
This paper introduces a modular atomic reasoning approach for scientific table claim verification, inspired by Cognitive Load Theory, which improves accuracy and generalization with less training data compared to existing models.
Contribution
It proposes a skill-chaining schema for atomic reasoning, creating a new benchmark, SciAtomicBench, and demonstrates superior performance over GPT-4o with minimal fine-tuning.
Findings
Outperforms GPT-4o's chain-of-thought method.
Achieves state-of-the-art results with only 350 fine-tuning examples.
Reduces cognitive load through modular reasoning components.
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…
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Taxonomy
TopicsTopic Modeling · Explainable Artificial Intelligence (XAI) · Artificial Intelligence in Healthcare and Education
