Researchers from the College of Computer Science, Sichuan University, and the Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education Chengdu, China, have introduced DREditor, a time-efficient method for adapting dense retrieval models to specific domains. DREditor achieves 100-300 times faster time efficiency and extends applicability to domain-specific scenarios. [50 words]
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Deploying Dense Retrieval Models for Enterprise Search
Deploying dense retrieval models is crucial in industries like enterprise search (ES), where a single service supports multiple enterprises. In ES, such as the Cloud Customer Service (CCS), personalized search engines are generated from uploaded business documents to assist customer inquiries. The success of ES providers relies on delivering time-efficient searching customization to meet scalability requirements. Failure to do so may lead to delays, impacting enterprise needs and causing a poor customer experience with potential business loss.
The Problem with Existing Models
The problem with the existing models, like implicit via long-time fine-tuning of retrieval models, is that they are time-consuming and may not provide optimal results. Longer training time is an issue as it consumes significant computational resources, leading to increased costs for infrastructure and energy consumption. Secondly, prolonged training times hinder the rapid development and experimentation cycles crucial for refining models and adapting them to changing requirements. Hence, the problem requires a new solution.
Introducing DREditor
The researchers from the College of Computer Science, Sichuan University and Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education Chengdu, China, have introduced DREditor, a time-efficient method for adapting off-the-shelf dense retrieval models to specific domains. Utilizing efficient linear mapping, DREditor calibrates output embeddings by solving a least squares problem with a specially constructed edit operator. In contrast to lengthy fine-tuning processes, experimental results demonstrate that DREditor achieves 100–300 times faster time efficiency across various datasets, sources, models, and devices while maintaining or surpassing retrieval performance.
Advantages of DREditor
DREditor exhibits substantial advantages in time efficiency, achieving a 100-300 times reduction in customization time compared to traditional fine-tuning methods while maintaining or surpassing retrieval performance. The approach outperforms implicit rule modification techniques. Experimental results highlight DREditor’s effectiveness across diverse datasets, sources, retrieval models, and computing devices. The research emphasizes the method’s contribution to filling a technical gap in embedding calibration, enabling cost-effective and efficient development of domain-specific dense retrieval models.
Practical AI Solutions
If you want to evolve your company with AI, stay competitive, and use it to your advantage, consider implementing DREditor for building a domain-specific dense retrieval model. Additionally, consider the AI Sales Bot from itinai.com/aisalesbot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages.
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