Tag
RimRule proposes a neuro-symbolic method that distills compact, interpretable rules from failure traces using the Minimum Description Length principle, improving LLM tool-use performance without modifying weights, and demonstrating rule portability across models.
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.
Hybrid-LoRA proposes a framework that selectively applies full fine-tuning to a small subset of modules while using LoRA for the rest, achieving performance near full fine-tuning with significantly lower computational cost. Experiments show improvements of up to 5.65% over existing parameter-efficient baselines.