Case Study: Find out how Privately was able to increase their SDO performance measures, such as 80% reduction in real-world failures and 10x faster development cycles — from roughly 2 weeks to 2 days.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
In-context learning
As users increasingly rely on Large Language Models (LLMs) to accomplish their daily tasks, their concerns about the potential leakage of private data by these models have surged.
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A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.
English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
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A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.
English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
Privately, a leading digital identity firm, has achieved the following results with Lakera for their computer vision system:
To understand the role that MLTest played in getting there, download the full case study:
Within only a few weeks of using MLTest, Privately was able to increase all of its project-related SDO (software delivery and operational) performance measures, including an 80% reduction in real-world failures and 10x faster development cycles (from roughly 2 weeks to 2 days).
Privately uses MLTest to:
Privately, a leading digital identity firm, provides computer vision products to make the world a safer place for minors. Their computer vision product, “FaceAssure”, is designed to estimate the ages of minors with industry-leading accuracy and is now one of the few recipients of a Challenge-25/EAL-2 certification for their age estimation technology.
Lakera’s MLTest played a crucial role in accelerating the development and certification of “FaceAssure”. You can read more about the journey in the case study below, as well as in Privately’s blog post.
Get in touch with david@lakera.ai to find out more about what Lakera can do for your team, or get started right away.
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