Small Language Model
News & Insights
Data parsing-annotating
In a data parsing/annotating use case, the goal is to extract and label relevant information from large text datasets for downstream tasks like classification, training data creation, or document tagging. In such scenarios, small language models (SLMs) and large language models (LLMs) perform differently in terms of efficiency, speed, and overall resource usage.
Use Case: Annotating Customer Feedback for Product Improvement
Scenario
A business wants to parse and annotate large volumes of customer feedback (e.g., surveys, reviews) to identify specific product issues, extract relevant themes, and assign appropriate tags. Both an SLM and an LLM are deployed to handle this task.
Key Metrics for Comparison
Latency: Time taken to parse and annotate a single customer feedback entry.
Memory Utilization: RAM usage during the annotation process.
Annotation Accuracy: Accuracy in tagging issues or themes (e.g., "delivery," "product quality," "customer service").
Throughput: Number of feedback entries processed per second.
Scalability: Ability to handle larger datasets with minimal overhead.
Metric
Model Size
Latency (average)
Memory Usage (RAM)
Compute Power
Annotation Accuracy
Throughput
Small Language Model (SLM)
60M parameters
0.05 seconds/entry
250 MB
CPU only
85%
20 entries/second
Large Language Model (LLM)
1.3B parameters
1.6 seconds/entry
8 GB
GPU/High-end CPU
94%
0.62 entries/second
Technical Insights
Latency: The SLM processes and annotates text entries in 0.05 seconds per entry, which is 32 times faster than the LLM's 1.6 seconds. This speed advantage becomes critical when dealing with high volumes of data, where even small delays can add up to hours of extra processing time.
Memory and Compute Efficiency: The SLM operates with just 250 MB of RAM, making it suitable for edge computing or environments with limited resources. In contrast, the LLM consumes over 8 GB of RAM, requiring a high-performance environment, such as a GPU-equipped cloud setup, to process data efficiently. This makes the LLM much harder to deploy at scale or on-premises without significant infrastructure.
Throughput: The SLM can handle 20 entries per second, providing significantly higher throughput than the LLM’s 0.62 entries per second. This means the SLM can parse and annotate large datasets much faster, making it suitable for real-time data parsing scenarios or situations where fast processing is crucial.
Annotation Accuracy: While the LLM achieves 94% accuracy in identifying and tagging complex themes or multi-faceted issues, the SLM offers a solid 85% accuracy, which is sufficient for straightforward annotations (e.g., “late delivery” or “defective product”). The additional accuracy of the LLM might only be necessary when parsing highly complex or nuanced feedback.
Business Insights
Cost Efficiency: Running an SLM is far more cost-effective than deploying an LLM. The SLM’s low memory and CPU requirements allow businesses to use existing on-premise hardware, reducing the need for expensive cloud infrastructure or GPUs. For businesses processing customer feedback or documents at scale, this means significant cost savings.
Faster Data Processing: The SLM processes 20 entries per second, offering near-instantaneous results when parsing and annotating customer feedback. This is especially valuable for businesses that need to quickly analyze data to respond to emerging issues, trends, or crises. The 32x faster latency ensures timely insights, which are critical for improving customer satisfaction or adjusting product features in response to feedback.
Scalability: An SLM’s lower resource usage and higher throughput make it easier to scale across multiple devices or environments. Businesses can implement SLMs across various touchpoints, such as in customer support systems or on embedded devices, without the need for additional infrastructure. This scalability allows companies to extract insights from customer feedback in real time or at a much larger scale without significant investment in infrastructure.
Good-Enough Accuracy: Although LLMs provide higher accuracy, the 85% accuracy of the SLM is sufficient for many use cases where precision isn’t paramount. For example, in feedback that is relatively easy to parse (e.g., clear issues such as “damaged packaging” or “late delivery”), the SLM can perform the job just as effectively but at a fraction of the cost and time.
Benchmarking Example:
Suppose a business processes 500,000 customer feedback entries daily.
SLM Processing Time: 0.05 seconds/entry → 41 minutes for the entire dataset.
LLM Processing Time: 1.6 seconds/entry → 9.2 days for the entire dataset.
In this scenario, the SLM processes the entire dataset in less than an hour, while the LLM would take over a week, demonstrating the efficiency and scalability of the SLM for daily, time-sensitive tasks.
Conclusion
For data parsing/annotating use cases, especially when dealing with large volumes of relatively straightforward data, small language models (SLMs) offer clear advantages over large language models (LLMs) in terms of speed, efficiency, and scalability. SLMs require significantly fewer resources, process data at a much higher throughput, and can deliver results faster with minimal infrastructure costs. While LLMs may offer higher accuracy, their increased latency, resource demands, and cost make them less practical for routine parsing and annotation tasks. SLMs provide an optimal balance of speed and performance, making them ideal for businesses that need to extract insights quickly and efficiently from large datasets.