AI Search Model
Integrating advanced retrieval algorithms for enhanced query understanding and user experience in search.
AI Search
Developing an AI-based conversational search model for enhanced retrieval.Implementing a conversational search system framework (SearchNet) requires deep model customization and complex training beyond GPT-3.5's fine-tuning capabilities. First, handling complex query understanding and context maintenance requires more powerful computing capabilities and flexible architecture design. Second, precise adjustment for multi-turn dialogue management and result generation requires more advanced fine-tuning permissions. Third, to ensure system reliability in various search scenarios, testing and validation must be conducted on models with sufficient scale. GPT-4's architectural features and performance advantages provide necessary technical support for this innovative application.
Search Model
This project focuses on constructing a conversational search model, integrating advanced retrieval algorithms, and validating performance across various queries and user needs for improved search experiences.
Deep Learning
We are designing deep learning-based retrieval algorithms, including intent recognition and relevance ranking, to enhance the effectiveness of our AI search model and improve user interaction.
AI Search Model
Integrating conversational AI for improved search capabilities and user experience across diverse queries.
Deep Learning Tools
Implementing advanced algorithms for intent recognition, relevance ranking, and effective result summarization.
GPT Integration
Seamlessly integrating searchnet with GPT architecture for rigorous experimental validation of model performance.
In-depth analysis to evaluate retrieval accuracy and optimize system performance based on user needs.
Performance Evaluation
