Large Language and Multimodal Models in Archaeological Science: A Review

Large Language Models (LLMs) and Multimodal Models (LMMs) are significantly influencing scientific research, including archaeology—a discipline dealing with uniquely complex, multimodal data. This comprehensive review systematically examines recent (2023+) applications of LLMs/LMMs in archaeology, covering ancient texts, artifacts, field data, and knowledge graphs. While current applications are often exploratory and fragmented, they demonstrate substantial potential for addressing long-standing archaeological challenges such as data heterogeneity, knowledge integration, and interpretive complexity. We argue that archaeology serves as a valuable “proving ground” for next-generation AI technologies due to its distinctive data characteristics (multimodal heterogeneity, sparsity, uncertainty) and high demands for robust knowledge reasoning and interpretability. This review critically analyzes technical approaches including fine-tuning methods (LoRA, PEFT), retrieval-augmented generation (RAG), and recent advances (RAFT, LongRoPE, Phi-3-mini) that enable efficient local deployment. We examine data, knowledge, technological, and ethical challenges, distinguishing between issues generic to machine learning and those specific to Transformer-based LLMs in heritage contexts. This review concludes by identifying prioritized future research directions for integrated “AI Archaeology,” emphasizing responsible AI principles and human-in-the-loop frameworks. This study offers cross-disciplinary insights for fostering deep, synergistic, and ethically sound AI integration within archaeological science and broader cultural heritage applications.