Beyond Case Law: Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA
arXiv:2604.06173v2 Announce Type: replace-cross Abstract: Legal QA benchmarks have predominantly focused on case law, overlooking the unique challenges of statute-centric regulatory reasoning. In statutory domains, relevant evidence is distributed across hierarchically linked documents, creating a statutory retrieval gap where conventional retrievers fail and models often hallucinate under incomplete context. We introduce SearchFireSafety, a structure- and safety-aware benchmark for statute-centric legal QA. Instantiated on fire-safety regulations as a representative case, the benchmark evalua
The proliferation of AI in legal tech is exposing limitations of current benchmarks, necessitating specialized solutions for complex, structure-aware domains like regulatory compliance.
This development highlights the critical need for AI systems to accurately interpret and apply statute-centric legal documents, potentially preventing hallucination and improving regulatory adherence.
The focus of legal AI benchmarks is shifting beyond case law to include the unique challenges of hierarchical, statute-based reasoning, improving the reliability of AI applications in compliance.
- · Legal AI developers
- · Regulatory compliance platforms
- · Legal firms adopting AI
- · Governments streamlining regulation
- · AI models without structure-aware retrieval
- · Compliance departments reliant on manual processes
- · Companies facing regulatory fines due to AI errors
Improved accuracy and reliability of AI in legal and regulatory compliance.
Reduced legal risks and operational costs for organizations deploying AI in regulated industries.
The acceleration of AI adoption for drafting and interpreting complex legal frameworks, potentially altering legal workflow paradigms.
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Read at arXiv cs.AI