Deploying dense retrieval models is crucial in industries like enterprise search (ES), where a single service supports multiple enterprises. In ES, reminiscent of the Cloud Customer Service (CCS), personalized search engines like google are generated from uploaded business documents to help customer inquiries. The success of ES providers relies on delivering time-efficient searching customization to satisfy scalability requirements. Failure to achieve this may result in delays, impacting enterprise needs and causing a poor customer experience with potential business loss.

The issue with the prevailing models, like implicit via long-time fine-tuning of retrieval models, is that they’re time-consuming and should not provide optimal results. Longer training time is a problem because it consumes significant computational resources, resulting in 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 issue requires a brand new solution.

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 display that DREditor achieves 100–300 times faster time efficiency across various datasets, sources, models, and devices while maintaining or surpassing retrieval performance. 

DREditor employs adapter fine-tuning and introduces a time-efficient approach by directly calibrating output embeddings using a linear mapping technique. It solves a specially constructed least squares problem to acquire an edit operator. The tactic significantly reduces customization time in comparison with traditional approaches, enhancing the generalization capability of DR models across specific domains. The post-processing step of DREditor’s matching rule editing involves a computation-efficient linear transformation powered by the derived edit operator𝑊𝑄𝐴.

DREditor exhibits substantial benefits in time efficiency, achieving a 100-300 times reduction in customization time in comparison with 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 tactic’s contribution to filling a technical gap in embedding calibration, enabling cost-effective and efficient development of domain-specific dense retrieval models.

To sum up, The 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 the DREditor, a domain-specific dense retrieval model time-efficiently. This approach facilitates timely customization for enterprise search providers, ensuring scalability and meeting time-sensitive demands. A noteworthy contribution is the mixing of emerging studies on embedding calibration into retrieval tasks. The tactic extends applicability to zero-shot domain-specific scenarios, showcasing its potential for cost-effective and efficient development of domain-specific DR models.

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