LLM Application in Text Information Detection and Extraction: A Case Study of Blue-Collar Recruitment Data Processing

This article presents a comprehensive case study on applying Large Language Models (LLM) to blue-collar recruitment data processing. The context involves addressing challenges in unstructured job posting data, including messy text descriptions, regional terminology variations, and ambiguous salary information.

The solution employs a multi-stage approach combining traditional rule-based cleaning with AI-powered processing. The workflow consists of four main phases: initial rule-based cleaning to extract basic elements (phone numbers, job types, addresses), AI-powered cleaning for multi-job splitting and structured data output, secondary rule-based cleaning to supplement missing data, and final rule-based filtering to ensure only valid job postings are published.

The article details three key LLM optimization strategies:

1. Prompt Engineering : Designing effective prompts with role assignment, clear instructions, structured frameworks, and iterative optimization. A comprehensive prompt example is provided for job posting analysis, including validation steps for external compliance, internal compliance, fund flow direction, income acquisition methods, and work location verification.

2. RAG (Retrieval Augmented Generation) : Combining prompt engineering with external data source retrieval to enhance output quality and reliability. The article explains RAG's three-step process: retrieval, augmentation, and generation, and discusses its potential application with knowledge bases for screening words, job types, and filtering terms.

3.

Model Fine-tuning

: Comparing full fine-tuning (FFT) and parameter-efficient fine-tuning (PEFT) approaches. The team uses LoRA-based SFT fine-tuning through Volcano Engine's model optimization service, with detailed steps for dataset creation, annotation, preprocessing, model configuration, and iterative evaluation.

The article includes practical examples demonstrating LLM's superiority over traditional keyword matching methods, showing how LLM can accurately understand context, extract key information, and integrate related content. Performance analysis compares different models (DeepSeek vs. fine-tuned models) using metrics like extraction accuracy, job posting rates, and classification precision.

Benefits achieved include significant cost reduction and efficiency improvement, with AI processing enabling handling of over 10,000 job postings daily compared to manual processing limits. Future plans include continuous model optimization, RAG enhancement, multi-model comparison, private deployment, and expansion to other business scenarios like issue identification.