As Large Language Models (LLMs) and multimodal models become more prevalent, ensuring their safety and detecting harmful outputs has become a top priority. I’ve been working extensively on evaluating the safety of these models, focusing on content safety ratings, harm categorization, and red teaming (text, audio, video). The goal is to identify and mitigate risks before these models are released into the wild.
## The Importance of Safety Checks
Developing robust safety frameworks is crucial to preventing harmful outputs, which can have serious consequences. It’s not just about avoiding controversy; it’s about creating models that are responsible, ethical, and respectful.
## In-House vs. External Frameworks
I’m curious to know how others are approaching LLM safety evaluations. Are you building your own red teaming and safety checks in-house, or relying on external frameworks? What’s working for you, and what’s not?
## Benchmarking Risks and Refining Models
By sharing our experiences and knowledge, we can create better, safer models. I’ve had the opportunity to work with teams at big tech companies, and the feedback has been overwhelmingly positive. Let’s swap notes and learn from each other.
## The Future of LLM Safety
As we continue to develop more advanced AI models, ensuring their safety will become increasingly important. By working together and sharing our expertise, we can create a safer, more responsible AI landscape.
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*Further reading: [Large Language Models: A Survey](https://arxiv.org/abs/2009.11732)*