Recruitment process support

In a recruitment process support use case, language models can assist HR teams by screening resumes, generating job descriptions, or summarizing candidate profiles. When comparing small language models (SLMs) and large language models (LLMs) in this context, the performance differences in efficiency, speed, and cost can significantly impact recruitment outcomes.

Use Case: Screening Resumes and Generating Candidate Summaries

Scenario

A company receives thousands of resumes for a job opening and needs to filter candidates based on job requirements. Both SLMs and LLMs are used to extract relevant skills and summarize candidate profiles for faster decision-making.

Key Metrics for Comparison

Latency: Time taken to process and summarize a candidate's resume.
Memory Usage: RAM is required to analyze resumes.
Accuracy: Precision in extracting relevant information (skills, experience) and generating summaries.
Throughput: Number of resumes processed per second.
Cost Efficiency: Compute resources required, impacting the overall cost of running the model.

Metric

Model Size
Latency (average)
Memory Usage (RAM)
Compute Power
Accuracy (Relevance)
Throughput

Small Language Model (SLM)

100M parameters
0.05 seconds/resume
250 MB
CPU only
85%
20 resumes/second

Large Language Model (LLM)

1.2B parameters
1.5 seconds/resume
9 GB
GPU/High-end CPU
96%
0.67 resumes/second

Technical Insights

Latency: The SLM processes each resume in 0.05 seconds, which is 30x faster than the LLM's 1.5 seconds. This speed advantage is critical for recruitment teams that handle hundreds or thousands of resumes in a day, allowing for faster candidate filtering and decision-making.
Memory and Compute Efficiency: The SLM uses 250 MB of RAM and runs efficiently on standard CPUs. In contrast, the LLM consumes 9 GB of RAM, necessitating high-end infrastructure such as GPUs or powerful CPUs, which can significantly increase hardware and cloud costs. The SLM’s low memory usage means it can be deployed across multiple machines or even on-premise systems without needing additional hardware investments.
Throughput: The SLM can process 20 resumes per second, allowing for rapid filtering in large recruitment drives. On the other hand, the LLM processes 0.67 resumes per second, making it much slower and impractical for real-time, high-volume recruitment tasks. For companies managing large recruitment pipelines, the SLM provides much higher throughput, leading to faster overall processing times.
Accuracy: While the LLM offers superior accuracy (96%) in extracting nuanced skills and generating detailed summaries, the SLM’s 85% accuracy is often sufficient for routine recruitment tasks where broad matching of qualifications and experience is required. The SLM can quickly identify key skills and work history, offering good-enough summaries that allow recruiters to narrow down candidates for further review.

Business Insights

Cost Efficiency: Running an SLM for resume screening is far more cost-effective than using an LLM. The SLM operates on standard infrastructure without the need for expensive cloud GPUs or high-end CPUs, making it ideal for companies looking to streamline their recruitment processes without a substantial increase in operational costs. This cost efficiency is particularly beneficial for small and medium-sized businesses that need to optimize recruitment spending.
Speed in Screening: The SLM’s ability to process resumes in 0.05 seconds per resume allows recruitment teams to filter thousands of candidates in real time, enabling quicker decision-making and faster responses to potential hires. This is especially critical in industries where top talent is in high demand, and swift action is needed to secure candidates.
Scalability: The SLM’s high throughput of 20 resumes per second makes it highly scalable for large recruitment campaigns, allowing companies to screen a high volume of candidates in a short time. Whether running a hiring spree for seasonal workers or conducting high-volume recruitment in a fast-growing company, the SLM can handle the workload efficiently without delays.
Good Enough Accuracy: While the LLM’s 96% accuracy is ideal for more nuanced positions or roles requiring deep skill analysis, the SLM’s 85% accuracy is sufficient for most routine recruitment tasks, such as filtering candidates based on specific qualifications or job titles. This level of accuracy is particularly suitable for industries where skills and experience are more straightforward to assess (e.g., retail, customer service).

Benchmarking Example

Consider a recruitment team needing to process 100,000 resumes for a large hiring campaign.

SLM Processing Time: 0.05 seconds/resume → 1.39 hours to process all resumes.
LLM Processing Time: 1.5 seconds/resume → 41.6 hours to process all resumes.

With the SLM completing the task in less than two hours, the recruitment team can make quick decisions and move candidates through the hiring pipeline faster, while the LLM’s slower processing time would delay decision-making by nearly two days.

Conclusion

For recruitment process support use cases, such as screening resumes and generating candidate summaries, small language models (SLMs) offer substantial advantages over large language models (LLMs) in terms of efficiency, speed, and cost-effectiveness. The SLM’s ability to process resumes quickly, its low memory usage, and its high throughput make it ideal for companies that need to automate recruitment tasks at scale. While LLMs provide better accuracy, the 85% accuracy of an SLM is sufficient for most recruitment tasks where broad filtering and matching are required.

Businesses can benefit from the SLM’s real-time capabilities and cost-effective deployment, allowing them to optimize the recruitment pipeline without the need for expensive infrastructure. This balance of efficiency and accuracy makes the SLM the preferred choice for high-volume, fast-paced recruitment processes.