Tag
This paper investigates parameter-efficient strategies for adapting large language models to 3D CT report generation, introducing RAD3D-Prefix, a lightweight diagnostic-prior conditioning framework that keeps the LLM frozen and requires minimal trainable parameters. It shows that freezing larger LLMs (~1B+) and training only lightweight projection layers provides a superior trade-off between performance, generalization, and computational efficiency.