Build High-Performance Text Classification Agents

Lamini

What is text classification

Text classification is the process of categorizing text into predefined categories based on its content. This involves using an LLM to automatically analyze and assign labels to pieces of text, such as emails, articles, social media posts, or any other textual data. Text classification is commonly used for sentiment analysis, topic detection, spam detection, and more. Practical applications of text classification include:

A grocery delivery service company tags products based on various attributes—category, dietary preference, sale items—to make it easier for customers to find the items they need.
An e-commerce retailer classifies product reviews by sentiment to understand how products are performing.
A customer support organization classifies incoming support tickets to route them to the right departments.
An airline classifies user feedback into 600 classes to identify changes to their service and understand user trends.
A financial services company stratifies user complaints into different degrees of urgency that follow compliance and regulatory turnaround.

Text classification challenges

Accurately classifying a large amount of content at scale is an age-old challenge in the Natural Language Processing (NLP) field. LLMs, with their rich linguistic capabilities and adaptability to a wide range of use cases hold a lot of promise, but challenges still persist:

Scalability. Scaling LLMs to handle a large number of classes is difficult due to the amount of data needed for labeling and computational requirements to store and retrieve the classes. Anecdotally, LLMs can often only handle 20 classes.
Accuracy. As the number of classes increases, accuracy tends to decrease. The LLM may confuse classes that are similar because it cannot detect nuances. The LLM may also hallucinate completely new classes as they are not trained to be calibrated. Anecdotally, LLMs can often only reach 80-85% accuracy, when some use cases require 9’s of reliability.
Latency. Large numbers of classes will increase latency during inference because of the high computational burden of stuffing the prompt with class context and sufficient examples.

Text classification approaches

Common approaches to text classification include zero-shot and few-shot prompting. However, these approaches have their limitations:

Zero-shot prompting is not sufficient for more complex tasks or those that require more specialized knowledge.
Few-shot prompting relies heavily on the quality, breadth, and diversity of examples given. While it may increase accuracy for simple tasks, it can struggle with more complex tasks, especially when it requires knowledge of proprietary terms and data.

Lamini Memory Tuning offers a number of advantages over other techniques.

Scalability. Lamini Classifier can handle 1000s of classes efficiently and precisely.
Accuracy. >95% accuracy on those 1000s of classes, not hallucinating on nonexistent classes. Get to 9s of reliability like 99.9% accuracy. This level of precision is crucial for applications that require fine-grained classification.
Latency and throughput. Lamini delivers extremely low latency, under 100ms, when distilling the LLM. Experience minimal wait times, even when dealing with large-scale classification. On throughput, you can process billions of requests per month efficiently even on a single GPU.

How CopyAI categorizes vast amounts of data with 100% accuracy

Once [the LLM built with Lamini] was ready, we tested it, and it was so easy to deploy to production. It allowed us to move really rapidly. - Chris Lu, Co-founder

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