On the Effectiveness-Fluency Trade-Off in LLM Conditioning: A Systematic Study

Researchers systematically evaluated various conditioning methods for large language models, revealing that effective concept injection or removal often compromises fluency. They also found that activation steering is less effective on instruction-tuned models compared to base models, while prompting and fine-tuning offer different trade-offs.