Reasoners or Translators? Contamination-aware Evaluation and Neuro-Symbolic Robustness in Tax Law

2026-05-18 · Source: cs.AI updates on arXiv.org · Field: Legal & Regulatory — Legal Technology (LegalTech), Regulatory Affairs & Government Relations · Depth: Expert, quick

Summary

A comprehensive empirical study investigates the performance of large language models (LLMs) in automated tax law reasoning, addressing concerns about data contamination. Researchers implemented a contamination detection protocol, revealing that LLM performance can be artificially inflated by contaminated data. The study systematically compared monolithic LLMs against hybrid neuro-symbolic systems, which translate statutory text into formal representations and utilize symbolic solvers for inference. A novel test suite was developed to evaluate generalization to unseen documents through case and rule variations. The findings suggest that legal reasoning is compositional and that neuro-symbolic frameworks provide a more robust and reliable foundation for legal AI, demonstrating improved generalization to unobserved legal scenarios.

Key takeaway

For legal professionals developing or deploying AI systems for complex tasks like tax law reasoning, you should prioritize neuro-symbolic frameworks over monolithic LLMs. These hybrid systems offer superior robustness and generalization to novel legal situations, mitigating risks associated with data contamination and ensuring more reliable outcomes in critical applications. Evaluate your AI solutions with test suites designed for compositional reasoning and unseen variations.

Key insights

Neuro-symbolic AI offers more robust and generalizable legal reasoning than monolithic LLMs, especially in tax law.

Principles

• Legal reasoning is inherently compositional.
• Data contamination inflates LLM performance.
• Hybrid systems improve generalization.

Method

The study used a contamination detection protocol and a novel test suite with case and rule variations to compare monolithic LLMs against neuro-symbolic systems for tax law reasoning.

In practice

• Implement contamination detection in legal AI.
• Consider neuro-symbolic for legal reasoning.
• Design test suites for generalization.

Topics

• Large Language Models
• Legal Reasoning
• Tax Law
• Data Contamination
• Neuro-Symbolic AI

Best for: AI Scientist, Legal Professional, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.