DoTA-RAG: Dynamic of Thought Aggregation RAG
Saksorn Ruangtanusak, Natthapath Rungseesiripak, Peerawat Rojratchadakorn, Monthol Charattrakool, Natapong Nitarach

TL;DR
DoTA-RAG is a retrieval-augmented generation system designed for fast, accurate access to large-scale web knowledge, improving answer correctness and efficiency through dynamic routing and optimized embedding models.
Contribution
The paper introduces DoTA-RAG, a novel three-stage retrieval pipeline with dynamic routing and enhanced embedding evaluation, enabling high-throughput, accurate web knowledge retrieval.
Findings
Answer correctness score improved from 0.752 to 1.478.
Achieved a 0.929 correctness score on the Live Challenge Day.
Maintains low latency while handling large, diverse datasets.
Abstract
In this paper, we introduce DoTA-RAG (Dynamic-of-Thought Aggregation RAG), a retrieval-augmented generation system optimized for high-throughput, large-scale web knowledge indexes. Traditional RAG pipelines often suffer from high latency and limited accuracy over massive, diverse datasets. DoTA-RAG addresses these challenges with a three-stage pipeline: query rewriting, dynamic routing to specialized sub-indexes, and multi-stage retrieval and ranking. We further enhance retrieval by evaluating and selecting a superior embedding model, re-embedding the large FineWeb-10BT corpus. Moreover, we create a diverse Q&A dataset of 500 questions generated via the DataMorgana setup across a broad range of WebOrganizer topics and formats. DoTA-RAG improves the answer correctness score from 0.752 (baseline, using LiveRAG pre-built vector store) to 1.478 while maintaining low latency, and it achieves…
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Taxonomy
TopicsFunctional Brain Connectivity Studies
