Evaluating Customized vs. Generalist Transformer-based Models for Legal Contract Classification
arXiv:2508.07849v2 Announce Type: replace Abstract: Despite advances in legal NLP, no comprehensive evaluation of Transformer-based models customized for legal tasks (referred to as `legal-specific' models in this paper) exists for contract classification tasks. To address this gap, we present an evaluation of 13 legal-specific transformer-based models on 3 English-language contract classification tasks and compare them with 9 generalist models. The results show that legal-specific models consistently outperform generalist models, especially on tasks requiring nuanced legal understanding. They
The proliferation of advanced Transformer-based models has reached a point where specialized applications, such as legal, are now demonstrating clear superiority over generalist AI.
This highlights the growing need for domain-specific AI training and models, rather than relying solely on general-purpose AI, to achieve high-performance results in complex professional fields.
The market for AI solutions in specialized sectors like legal will increasingly demand and reward tailored models that offer superior accuracy and nuance compared to broader AI applications.
- · Legal AI development firms
- · Specialized data annotation services
- · Legal departments adopting custom AI
- · Generalist LLM providers in specialized markets
- · Law firms not adopting AI
Legal-specific AI models will see accelerated adoption due to demonstrated performance gains in contract classification.
This success will spur the development of other domain-specific AI models across various highly specialized industries.
The competitive advantage of enterprises that leverage highly customized AI will increase significantly, leading to a wider performance gap with those relying on generalized solutions.
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Read at arXiv cs.CL