Atomic Reasoning for Scientific Table Claim Verification
Journal:
arXiv
Published Date:
Jun 8, 2025
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.
