Summary of Towards Safety and Helpfulness Balanced Responses Via Controllable Large Language Models, by Yi-lin Tuan et al.
Towards Safety and Helpfulness Balanced Responses via Controllable Large Language Models
by Yi-Lin Tuan, Xilun Chen, Eric Michael Smith, Louis Martin, Soumya Batra, Asli Celikyilmaz, William Yang Wang, Daniel M. Bikel
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As large language models (LLMs) become widely available, the delicate balance between safety and helpfulness poses significant user experience implications. Prioritizing one over the other may have unintended consequences, such as reducing engagement or causing harm. To mitigate these risks, we propose a framework for controlling both safety and helpfulness in LLMs, exploring training-free and fine-tuning methods that don’t require additional human annotations. Our experiments show that our approach can rewind learned models and unlock controllability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are changing the way people interact with computers. But how do we make sure these models are both helpful and safe? Sometimes, making a model safer might make it less helpful, or vice versa. We need to find a balance between safety and helpfulness. In this paper, we explore new ways to control large language models so they’re both helpful and safe. We show that our approach can help keep the good things about these models while minimizing the bad. |
Keywords
» Artificial intelligence » Fine tuning
