NeuralSearchX: Serving a Multi-billion-parameter Reranker for Multilingual Metasearch at a Low Cost
Thales Sales Almeida, Thiago Laitz, Jo\~ao Ser\'odio, Luiz Henrique, Bonifacio, Roberto Lotufo, Rodrigo Nogueira

TL;DR
NeuralSearchX introduces a cost-effective, large multilingual reranker for metasearch engines that outperforms Google API in domain-specific retrieval tasks, with competitive efficiency and near state-of-the-art results.
Contribution
The paper presents NeuralSearchX, a unified, large reranking model architecture optimized for cost and performance in multilingual metasearch applications.
Findings
Outperforms Google API in domain-specific nDCG@10 scores
Achieves competitive QPS with lower costs
Close to state-of-the-art on public benchmarks
Abstract
The widespread availability of search API's (both free and commercial) brings the promise of increased coverage and quality of search results for metasearch engines, while decreasing the maintenance costs of the crawling and indexing infrastructures. However, merging strategies frequently comprise complex pipelines that require careful tuning, which is often overlooked in the literature. In this work, we describe NeuralSearchX, a metasearch engine based on a multi-purpose large reranking model to merge results and highlight sentences. Due to the homogeneity of our architecture, we could focus our optimization efforts on a single component. We compare our system with Microsoft's Biomedical Search and show that our design choices led to a much cost-effective system with competitive QPS while having close to state-of-the-art results on a wide range of public benchmarks. Human evaluation on…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Stream Mining Techniques · Innovative Microfluidic and Catalytic Techniques Innovation · Mobile Crowdsensing and Crowdsourcing
