How to identify undesirable behaviors by LLMs? (1 minute read)

Bing recently integrated ChatGPT into their search engine, and few outputs generated were horrifying as they were either unethical, biased, or toxic. So what can be done to improve in this direction and build more consumer trust around these LLMs?

Found an interesting blogpost by Nazneen Rajani, Nathan Lambert and Lewis Tunstall about Red Teaming LLMs. (Link Below)

The blog post discusses the potentially undesirable behaviors exhibited by Large Language Models (LLMs) and the need for red-teaming, a form of evaluation that elicits model vulnerabilities that might lead to harmful behaviors.

Red-teaming can reveal limitations in LLMs that can cause upsetting user experiences or enable harm by aiding violence or other unlawful activity. The findings from past work on red-teaming LLMs are presented, and future directions for research in this field are outlined. The authors call for collaboration among LLM researchers and developers to address these concerns and create a safe and friendly world.

This field of research will become more mainstream in the near future when several companies and startups would have integrated their products/services with generative AI solutions.

Read more on the blog : https://huggingface.co/blog/red-teaming