Trend identification

In a trend identification use case, language models are employed to analyze large amounts of data from social media, market reports, or customer reviews to spot emerging patterns or trends. Here, we compare small language models (SLMs) and large language models (LLMs) in terms of their efficiency, speed, and overall performance for trend identification tasks.

Use Case: Identifying Emerging Market Trends from Social Media Data

Scenario

A retail company wants to identify emerging trends from social media posts related to fashion and consumer preferences. The goal is to extract keywords, detect sentiment, and summarize trends from a dataset of 100,000 social media posts. Both SLMs and LLMs are used to process the data.

Key Metrics for Comparison

Latency: Time taken to process and analyze each post.
Memory Usage: RAM is required to handle trend analysis.
Accuracy: Precision in detecting relevant trends and keywords.
Throughput: Number of posts processed per second.
Cost Efficiency: Overall cost based on infrastructure and processing time.

Metric

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

Small Language Model (SLM)

150M parameters
0.03 seconds/post
350 MB
CPU only
82%
33 posts/second

Large Language Model (LLM)

1.7B parameters
1.7 seconds/post
12 GB
GPU/High-end CPU
95%
0.59 posts/second

Technical Insights

Latency: The SLM takes 0.03 seconds to process each social media post, compared to the LLM's 1.7 seconds per post. This results in a performance improvement of over 50x faster processing speed. For large datasets, the SLM’s faster processing time is critical, as it can analyze trends in real-time or near-real time, whereas the LLM would take significantly longer.
Memory and Compute Efficiency: The SLM operates with a memory footprint of 350 MB, making it suitable for use on standard CPUs. The LLM, on the other hand, requires 12 GB of RAM, necessitating GPU resources or high-end CPU instances. For companies with limited computing resources, the SLM offers a far more cost-effective solution, without the need for specialized hardware.
Throughput: The SLM processes 33 social media posts per second, whereas the LLM processes only 0.59 posts per second. The higher throughput of the SLM allows for much faster analysis, particularly important when working with large datasets (e.g., millions of posts). This makes the SLM ideal for real-time trend monitoring in industries where timely insights are crucial.
Accuracy: While the LLM achieves a higher accuracy (95%) in detecting trends and extracting keywords, the SLM’s 82% accuracy is still robust enough for many business use cases. The lower accuracy is balanced by the faster processing time and reduced resource requirements, making the SLM more practical for large-scale, time-sensitive analysis.

Business Insights

Cost Efficiency: For a business looking to identify trends from social media or customer data, the SLM provides a far more cost-effective solution. It runs on standard hardware (like a regular server or even on-premise setups) and requires much less memory, while the LLM needs expensive cloud instances with GPUs or high-end CPUs, leading to higher operational costs. This makes the SLM ideal for small to medium-sized businesses or organizations working with a limited budget.
Speed in Trend Identification: The SLM’s ability to process 33 posts per second allows businesses to detect emerging trends quickly. For industries like fashion or retail, where trends can change rapidly, this high speed ensures that companies can react in real-time. The LLM, processing 0.59 posts per second, might offer slightly more accurate insights but would take much longer to generate results, which may delay decision-making and action.
Scalability: The high throughput of the SLM makes it highly scalable for large datasets, such as millions of customer reviews, social media posts, or product mentions. This makes it perfect for companies running large-scale market research or social listening campaigns. The ability to handle large volumes of data quickly ensures that businesses can stay ahead of consumer trends and make data-driven decisions faster than competitors.
Good Enough Trend Detection: While the LLM offers slightly better accuracy, the SLM’s 82% accuracy in trend detection is more than sufficient for many business applications. It can effectively highlight emerging trends, common customer complaints, or popular products, allowing businesses to take action even without the highest precision. For businesses focused on high-level insights rather than extremely granular analysis, the SLM provides a pragmatic trade-off between speed and accuracy.

Benchmarking Example

Consider a company needing to analyze 1 million social media posts to detect consumer trends.

SLM Processing Time: 0.03 seconds/post → 8.3 hours to process 1 million posts.
LLM Processing Time: 1.7 seconds/post → 19.7 days to process 1 million posts.

With the SLM processing the data in just over 8 hours, the company can identify emerging trends in near real-time, whereas the LLM would take almost 3 weeks, which may lead to missing crucial market shifts or consumer preferences.

Conclusion

For trend identification use cases, such as analyzing social media posts or customer reviews to spot emerging market trends, small language models (SLMs) offer significant advantages over large language models (LLMs) in terms of speed, cost-efficiency, and scalability. The SLM’s ability to process large amounts of data in real-time makes it ideal for businesses that need to quickly adapt to changing consumer preferences or industry trends.

While LLMs offer higher accuracy, the SLM’s 82% accuracy is often sufficient for broad trend identification, especially in industries like fashion, retail, and marketing where speed is more critical than minute details. For companies that prioritize timely insights and resource efficiency, the SLM provides a highly effective solution for automating trend analysis and making data-driven decisions in real time.