Discover how companies are responsibly integrating AI in production. This invite-only event in SF will explore the intersection of technology and business. Find out how you can attend here.
In the age of generative AI, the safety of large language models (LLMs) is just as important as their performance at different tasks. Many teams already realize this and are pushing the bar on their testing and evaluation efforts to foresee and fix issues that could lead to broken user experiences, lost opportunities and even regulatory fines.
But, when models are evolving so quickly in both open and closed-source domains, how does one determine which LLM is the safest to begin with? Well, Enkrypt has the answer: a LLM Safety Leaderboard. The Boston-based startup, known for offering a control layer for the safe use of generative AI, has ranked LLMs from best to worst, based on their vulnerability to different safety and reliability risks.
The leaderboard covers dozens of top-performing language models, including the GPT and Claude families. More importantly, it provides some interesting insights into risk factors that might be critical in choosing a safe and reliable LLM and implementing measures to get the best out of them.
Understanding Enkrypt’s LLM Safety Leaderboard
When an enterprise uses a large language model in an application (like a chatbot), it runs constant internal tests to check for safety risks like jailbreaks and biased outputs. Even a tiny error in this approach could leak personal information or return biased output, like what happened with Google’s Gemini chatbot. The impact could be even bigger in regulated industries like fintech or healthcare.
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Discover how companies are responsibly integrating AI in production. This invite-only event in SF will explore the intersection of technology and business. Find out how you can attend here.
In the age of generative AI, the safety of large language models (LLMs) is just as important as their performance at different tasks. Many teams already realize this and are pushing the bar on their testing and evaluation efforts to foresee and fix issues that could lead to broken user experiences, lost opportunities and even regulatory fines.
But, when models are evolving so quickly in both open and closed-source domains, how does one determine which LLM is the safest to begin with? Well, Enkrypt has the answer: a LLM Safety Leaderboard. The Boston-based startup, known for offering a control layer for the safe use of generative AI, has ranked LLMs from best to worst, based on their vulnerability to different safety and reliability risks.
The leaderboard covers dozens of top-performing language models, including the GPT and Claude families. More importantly, it provides some interesting insights into risk factors that might be critical in choosing a safe and reliable LLM and implementing measures to get the best out of them.
Understanding Enkrypt’s LLM Safety Leaderboard
When an enterprise uses a large language model in an application (like a chatbot), it runs constant internal tests to check for safety risks like jailbreaks and biased outputs. Even a tiny error in this approach could leak personal information or return biased output, like what happened with Google’s Gemini chatbot. The impact could be even bigger in regulated industries like fintech or healthcare.
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