Even Small Reasoners Should Quote Their Sources: Introducing the
  Pleias-RAG Model Family
We introduce a new generation of small reasoning models for RAG, search, and
source summarization. Pleias-RAG-350m and Pleias-RAG-1B are mid-trained on a
large synthetic dataset emulating the retrieval of a wide variety of
multilingual open sources from the Common Corpus. They provide native support
for citation and grounding with literal quotes and reintegrate multiple
features associated with RAG workflows, such as query routing, query
reformulation, and source reranking. Pleias-RAG-350m and Pleias-RAG-1B
outperform SLMs below 4 billion parameters on standardized RAG benchmarks
(HotPotQA, 2wiki) and are competitive with popular larger models, including
Qwen-2.5-7B, Llama-3.1-8B, and Gemma-3-4B. They are the only SLMs to date
maintaining consistent RAG performance across leading European languages and
ensuring systematic reference grounding for statements. Due to their size and
ease of deployment on constrained infrastructure and higher factuality by
design, the models unlock a range of new use cases for generative AI.
680fa50d618d5374f2c39ca9
Pierre-Carl Langlais
admin_assigned
2025-04-29T12:42:46.025Z
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Pierre-Carl Langlais
user
Pclanglais
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Pavel Chizhov
admin_assigned
2025-04-29T12:42:52.583Z
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Pavel Chizhov
user
pchizhov
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Mattia Nee
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Carlos Rosas Hinostroza
claimed_verified
2025-05-05T14:07:15.106Z
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Carlos Rosas
user
carlosrosas
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Matthieu Delsart
admin_assigned
2025-04-29T12:43:10.648Z
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Matthieu Delsart
user
mdelsart
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Irène Girard
admin_assigned
2025-04-29T12:43:16.361Z
660ef82427d13dc2ca52d152
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Irène Girard
user
Ip2j
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Othman Hicheur
680fa50d618d5374f2c39cb0
Anastasia Stasenko
admin_assigned
2025-04-29T12:43:27.660Z
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Anastasia Stasenko
user
anastasiastasenko
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Ivan P. Yamshchikov
admin_assigned
2025-04-29T12:43:33.420Z
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/avatars/336b629180c70b5fd3d49865c2ee0eb9.svg
Ivan P. Yamshchikov
user
kroniker
Even Small Reasoners Should Quote Their Sources: Introducing the
  Pleias-RAG Model Family
We introduce a new generation of small reasoning models for RAG, search, and
source summarization. Pleias-RAG-350m and Pleias-RAG-1B are mid-trained on a
large synthetic dataset emulating the retrieval of a wide variety of
multilingual open sources from the Common Corpus. They provide native support
for citation and grounding with literal quotes and reintegrate multiple
features associated with RAG workflows, such as query routing, query
reformulation, and source reranking. Pleias-RAG-350m and Pleias-RAG-1B
outperform SLMs below 4 billion parameters on standardized RAG benchmarks
(HotPotQA, 2wiki) and are competitive with popular larger models, including
Qwen-2.5-7B, Llama-3.1-8B, and Gemma-3-4B. They are the only SLMs to date
maintaining consistent RAG performance across leading European languages and
ensuring systematic reference grounding for statements. Due to their size and
ease of deployment on constrained infrastructure and higher factuality by
design, the models unlock a range of new use cases for generative AI.
Two new mid-sized reasoning models, Pleias-RAG-350m and Pleias-RAG-1B, perform competitively on RAG benchmarks and support multilingual citation and grounding.
retrieval-augmented generation RAG query routing query reformulation source reranking standardized RAG benchmarks HotPotQA 2wiki Qwen-2.5-7B Llama-3.1-8B Gemma-3-4B multilingual support factuality generative AI