Ancient Cuneiform, Modern AI: Why Standard Translation Models Fail for Bronze Age Records

Large language models excel at generating fluent text but catastrophically fail at literal accuracy—a critical flaw when translating legal and financial documents where every name and number matters. This project exposes how modern AI's hallucination problem undermines practical historical applications and proves that solving ancient languages requires custom architectures designed for non-modern text patterns. With just 1,500 training pairs, a specialized AI pipeline outperformed standard models, demonstrating that domain-specific solutions beat general-purpose systems even with minimal data.