DoTA-RAG: Dynamic of Thought Aggregation RAG
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
a 0.929 correctness score on the Live Challenge Day. These results highlight
DoTA-RAG's potential for practical deployment in domains requiring fast,
reliable access to large and evolving knowledge sources.
6850cba15e07650ecce88fce
Saksorn Ruangtanusak
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2025-06-17T07:21:59.261Z
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Saksorn Ruangtanusak
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saksornr
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Natthapath Rungseesiripak
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Peerawat Rojratchadakorn
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2025-06-17T15:13:12.119Z
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Peerawat
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boat1603
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Monthol Charattrakool
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2025-06-17T01:57:53.939Z
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Monthol Charattrakool
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montholscbx
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Natapong Nitarach
extracted_confirmed
2025-06-23T11:08:26.608Z
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Natapong Nitarach (Schwyter)
user
natnitaract
DoTA-RAG: Dynamic of Thought Aggregation RAG
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
a 0.929 correctness score on the Live Challenge Day. These results highlight
DoTA-RAG's potential for practical deployment in domains requiring fast,
reliable access to large and evolving knowledge sources.
DoTA-RAG improves retrieval and generation accuracy over massive web datasets using a dynamic routing pipeline and optimized embedding models, achieving high correctness scores while maintaining low latency.
RAG DoTA-RAG query rewriting dynamic routing specialized sub-indexes multi-stage retrieval ranking embedding models re-embedding FineWeb-10BT Q&A dataset DataMorgana LiveRAG Live Challenge Day