Efficient Legal Document Analysis Using Reasoning-Enhanced Language Models
Keywords:
legal language models, reasoning-enhanced transformers, retrieval-augmented generation, legal informatics, auditability, sustainable deploymentAbstract
The analysis of legal documents presents a distinctive combination of linguistic complexity, domain-specific reasoning requirements, and institutional accountability demands. Although large language models have improved performance on many legal text tasks, their direct application in high-stakes legal workflows raises significant challenges related to inference cost, long-document processing, explainability, fairness, and auditability. This paper examines the design space of reasoning-enhanced language models for efficient legal document analysis from a systems perspective. It considers architectural strategies such as retrieval-augmented generation, domain-adapted encoders, long-context transformers, and lightweight reasoning path filtering. Rather than focusing on a single benchmark, the discussion emphasizes structural trade-offs among accuracy, latency, energy consumption, and governance overhead. The paper further addresses data provenance, knowledge integration, robustness under distribution shift, and the institutional mechanisms needed to ensure responsible deployment. We argue that efficient legal AI systems must integrate reasoning improvements with infrastructure-aware design and policy-compatible documentation. A central theme is that reasoning enhancements should not be treated solely as model-level phenomena; they must be embedded within broader socio-technical architectures that include human review, audit trails, and organizational controls. The analysis highlights how small-parameter models with selective reasoning alignment can reduce resource burdens while preserving the interpretability expected in legal settings. The conclusion identifies open challenges and future directions for sustainable and accountable legal document analysis systems.
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