Fine-Tuned SLMs Help Checkr Optimize Background Checks

Checkr, a technology company founded in 2014, specializes in modernizing background checks using AI and machine learning to improve efficiency, inclusivity, and transparency. At the LLMOps Summit in San Francisco, a Checkr representative shared their experience in building a language model-based system to automate the adjudication of background checks, significantly reducing costs and manual reviews. The team experimented with various large language models (LLMs), including GPT-4 and fine-tuned smaller models like Llama-2-7b, achieving improved accuracy and efficiency with reduced latency and cost. They utilized Predibase for fine-tuning, which provided the best performance and reliability, enabling Checkr to scale and expand its use cases while maintaining compliance and transparency in hiring practices. By leveraging Low-Rank Adaptation (LoRA) and Predibase's infrastructure, Checkr successfully optimized their models for production, resulting in a 5X cost reduction compared to previous methods.

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