Multi-agent architectures based on large-scale low-generation language models for solving complex legal problems: A comparative study

This article presents a comparative analysis of five multi-agent architectures based on large, low-generation language models for solving complex legal problems. The study was conducted on a specially prepared dataset of 25 questions of five difficulty levels on Russian family and civil law. Architectures of varying complexity were tested: from a simple lawyer-agent to extended ensembles with a dispatcher and a "jury" system. The main evaluation metrics were the average response quality score, token consumption, economic cost, and efficiency coefficient. The results revealed significant differences between the architectures: Option 5 demonstrated the best quality (6.44 points), but Option 1 proved the most effective with a coefficient of 49.46. Complex architectures required 10-15 times more tokens with an insignificant increase in quality. Analysis by complexity levels revealed that multi-agent systems are most effective for problematic situations and conflicts of laws, while simpler architectures are sufficient for typical tasks. The study provides scientifically based recommendations for selecting optimal architectural solutions for legal advisory systems, balancing quality and cost-effectiveness.