Inside the Best Language Model for Soft Skill Measurement
HiringBranch put their soft skills AI technology to the test. Does it read soft skills more accurately with proprietary data or the nearest best open-source language models?
Organizations need to be able to measure soft skills accurately during the hiring process in order to take advantage of the skills-based hiring trend that’s happening now.
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What’s Inside?
Packed with rich insights, this is an exclusive look inside HiringBranch data science. This study delivers hard proof around algorithm accuracy while revealing significant implications for high-volume hiring teams.
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Public datasets available to train algorithms may not be appropriate for soft skill measurement.
With enough of the right data, smaller datasets may lead to more accurate results.
A language model with greater accuracy will yield more effective hiring outcomes.
HiringBranch's language model measures soft skills with 98% accuracy.
Accurate Soft Skill Measurement Matters
Soft skills can and should be measured accurately, whether that is a reality for most organizations today or not. Soft skills account for 85% of career success!
Skills-based hiring (measuring both soft and hard skills instead of degrees, experience, etc.) is a priority for approximately three-quarters of recruiting pros because it has been proven to improve candidate quality, reduce bad hire rates, increase revenue, and more.
Skills-based hiring (measuring both soft and hard skills instead of degrees, experience, etc.) is a priority for approximately three-quarters of recruiting pros because it has been proven to improve candidate quality, reduce bad hire rates, increase revenue, and more.
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78%
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Acknowledgement
91%
Empathy
75%
Positive Language
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