Reasoners or Translators? Contamination-aware Evaluation and Neuro-Symbolic Robustness in Tax Law
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Computer Science > Artificial Intelligence
Title:Reasoners or Translators? Contamination-aware Evaluation and Neuro-Symbolic Robustness in Tax Law
Abstract:Recent advances in large language models (LLMs) have significantly enhanced automated legal reasoning. Yet, it remains unclear whether their performance reflects genuine legal reasoning ability or artifacts of data contamination. We present a comprehensive empirical study of tax law reasoning approaches and implement a contamination detection protocol to rigorously assess LLM reliability. We show that performance can be inflated by contamination. Building on this analysis, we conduct a systematic evaluation, comparing monolithic LLMs with hybrid systems that translate statutory text into formal representations and delegate inference to symbolic solvers. We build a novel test suite designed to probe generalization to unseen documents via case and rule variations. Our findings indicate that legal reasoning is inherently compositional and that neuro-symbolic frameworks offer a more reliable and robust foundation for legal AI, as well as improved generalization to unobserved situations.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.16052 [cs.AI] |
| (or arXiv:2605.16052v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16052
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Parisa Kordjamshidi [view email][v1] Fri, 15 May 2026 15:20:16 UTC (129 KB)
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