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LLMs struggle with contradictory tax data, but self-checks improve reliability

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the reliability of large language models (LLMs) in tax reasoning systems, particularly when faced with defective inputs such as missing or contradictory facts. While LLMs can accurately compute tax liabilities on well-formed cases, their performance degrades significantly with imperfect data. The study found that models often compute through contradictions rather than abstaining, but can effectively identify these issues when prompted to verify the input. By integrating a verification step, the models' ability to abstain from defective inputs was largely recovered with minimal impact on accuracy. AI

IMPACT Highlights the need for robust error detection in LLMs beyond benchmark accuracy, crucial for reliable AI deployment in sensitive applications.

RANK_REASON Research paper published on arXiv detailing LLM capabilities and limitations in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle with contradictory tax data, but self-checks improve reliability

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Research paper published on arXiv detailing LLM capabilities and limitations in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Albert Sadowski, Jaros{\l}aw A. Chudziak ·

    Solving versus Verifying: Catching Contradictions in Tax Reasoning Systems

    arXiv:2609.05928v1 Announce Type: cross Abstract: Large language models now compute correct tax liabilities on over 90% of well-formed cases in statutory benchmarks, which makes them candidates for the tax-advisory and compliance systems that consume such an answer directly. Real…

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