Object Material Generation with Intent-Based Prompting for E-Commerce
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Solution Overview
Problem
Existing methods for generating product materials in e-commerce scenarios are labor-intensive and inefficient, failing to adapt quickly to evolving application requirements due to reliance on fixed datasets for fine-tuning large language models.
Innovation Solution
An object material generation method that performs intent recognition on user instructions, formats prompts based on intent recognition results, and triggers a pre-fine-tuned object material generation model to produce materials, eliminating the need for scenario-specific fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual crafting methods are used to generate product materials, then the quality and precision of materials can be maintained, but the productivity and efficiency are significantly reduced
Solution Approach 1:
The system enables automated self-service generation of product materials through the material generation model. The model automatically processes product information and generates various materials (descriptions, titles, tags) without requiring manual intervention, thus maintaining quality while significantly improving productivity.
Solution Approach 2:
The patent replaces the mechanical manual crafting process with an automated AI-based material generation model. This substitution transforms the manual writing and editing process into an automated computational process that can rapidly generate high-quality product materials.
2Device complexity
If a fixed dataset is used to fine-tune the LLM model, then the model structure and training process can be simplified, but the adaptability to evolving application requirements deteriorates
Solution Approach 1:
The patent implements a dynamic fine-tuning mechanism where the model can be retrained with updated datasets as application requirements evolve. The system supports incremental fine-tuning with new data, allowing the model to adapt to changing e-commerce scenarios without requiring complete retraining, thus balancing complexity and adaptability.
Solution Approach 2:
The material generation model is designed with universal applicability across different e-commerce scenarios and product categories. By fine-tuning the base LLM with domain-specific data, the model gains the ability to handle diverse material generation tasks (product descriptions, titles, tags, recommendations) while maintaining a single unified model structure.
3Adaptability or versatility
If frequent model fine-tuning is performed to meet evolving requirements, then the adaptability improves, but the loss of time and computational resources increases
Solution Approach 1:
The system performs preliminary fine-tuning of the material generation model with comprehensive e-commerce domain data before deployment. This preliminary preparation allows the model to handle a wide range of scenarios out-of-the-box, reducing the frequency and urgency of subsequent fine-tuning operations and minimizing time loss.
Solution Approach 2:
The patent implements continuous fine-tuning capabilities where the model can be incrementally updated with new data in the background without disrupting the generation service. This allows the model to continuously adapt to evolving requirements while maintaining operational continuity, thus reducing the impact of fine-tuning on service availability.
4Productivity
If automated generation methods are used to improve efficiency, then the productivity increases, but the manufacturing precision and quality control may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where generated materials can be evaluated and refined. The material generation model learns from feedback signals (user interactions, conversion rates, manual corrections) to continuously improve the quality of generated content, ensuring that automated generation maintains high precision while delivering high productivity.
Data Source
AI summary
Embodiments of the present application provide an object material generation method, system, model fine-tuning method, electronic device, and storage medium. The object material generation method includes: performing intent recognition on instruction information indicating the generation of an object material to obtain an intent recognition result, wherein the intent recognition result includes a material generation scenario and/or key information of the object; in response to the intent recognition result meeting a preset condition, formatting a preset prompt template based on the intent recognition result to generate a prompt; in response to the intent recognition result not meeting the preset conditions, generating a prompt based on the instruction information using a pre-fine-tuned prompt generation model; triggering a preset object material generation model based on the generated prompt to produce the object material.