Towards Safety and Helpfulness Balanced Responses via Controllable Large Language Models
<|reference_start|>Towards Safety and Helpfulness Balanced Responses via Controllable Large Language Models: As large language models (LLMs) become easily accessible nowadays, the trade-off between safety and helpfulness can significantly impact user experience. A model that prioritizes safety will cause users to feel less engaged and assisted while prioritizing helpfulness will potentially cause harm. Possible harms include teaching people how to build a bomb, exposing youth to inappropriate content, and hurting users' mental health. In this work, we propose to balance safety and helpfulness in diverse use cases by controlling both attributes in LLM. We explore training-free and fine-tuning methods that do not require extra human annotations and analyze the challenges of controlling safety and helpfulness in LLMs. Our experiments demonstrate that our method can rewind a learned model and unlock its controllability.<|reference_end|>
@article{tuan2024towards,
    title={Towards Safety and Helpfulness Balanced Responses via Controllable Large
  Language Models},
    author={Yi-Lin Tuan, Xilun Chen, Eric Michael Smith, Louis Martin, Soumya
  Batra, Asli Celikyilmaz, William Yang Wang, Daniel M. Bikel},
    journal={arXiv preprint arXiv:2404.01295},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01295},
    primaryClass={cs.CL cs.AI}
}