Practice of Automatic Classification System for Customer Service Work Orders Based on Large Language Models

This article shares practical experience on building an automatic classification system for customer service work orders using Large Language Models (LLM). It aims to address the pain points of traditional manual classification, such as low efficiency, inconsistent standards, and high labor costs. By leveraging LLM's advantages like semantic understanding and few-shot learning, it improves customer service processing efficiency and reduces manual annotation costs. It also covers system architecture design, key implementation considerations, effect evaluation and optimization, and future development directions.

Customer service teams need to handle customer inquiry work orders from multiple channels such as emails, online chats, and social media every day, including types like product inquiries, technical support, complaints and suggestions, and billing issues. Manual classification has three main pain points: slow response speed with severe backlogs during peak periods; difficulty in unifying classification standards with differences in judgments among different customer service staff; and high labor costs, with a lot of time spent on repetitive work.

Compared with traditional rule-based or shallow machine learning classification methods, LLM has three major advantages: 1. Strong semantic understanding ability, which can accurately grasp the intent and emotion of work orders and handle ambiguous or non-standard descriptions; 2. Support for zero-shot/few-shot learning, which allows launching without a large amount of labeled data, lowering the implementation threshold; 3. Strong generalization ability, which is better than traditional solutions in adapting to new work order types or changes in business scenarios.

The system architecture includes four core modules: Data preprocessing module (cleaning and standardizing original work orders, removing sensitive information); Feature extraction module (using LLM to convert text into high-dimensional semantic vectors); Classification decision module (predicting categories based on features, which can adopt direct classification or retrieval-augmented generation strategies); Result post-processing module (confidence calibration, multi-label processing, triggering manual review).

During implementation, attention should be paid to: Model selection (open-source models like Llama/Qwen ensure data privacy, while commercial APIs are highly user-friendly); Classification system follows the MECE principle (Mutually Exclusive, Collectively Exhaustive) to avoid category overlap; Retain the "to be manually confirmed" category to divert work orders that the model is uncertain about to ensure service quality.

After the system goes online, an evaluation system needs to be established: technical indicators (accuracy, recall, F1 score) + business indicators (reduction ratio of average processing time, changes in customer satisfaction, degree of reduction in manual workload). Continuous optimization can be achieved through a feedback loop (manual correction results are fed back to training data) and regular analysis of error cases to identify model blind spots and improve.

Future outlook: Multimodal LLM will support the processing of multiple forms of content such as text, images, and voice; combined with knowledge graphs, the system can automatically associate solutions or historical cases; integrate with intelligent routing, automatic replies, knowledge base retrieval, etc., to build a complete intelligent customer service solution.