title: Even Small Reasoners Should Quote Their Sources: Introducing the
    Pleias-RAG Model Family
summary: 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.
paper_authors._id: 680fa50d618d5374f2c39ca9
paper_authors.name: Pierre-Carl Langlais
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:42:46.025Z
paper_authors._id: 64ce091a9e9ca8123d7a42b0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64ce091a9e9ca8123d7a42b0/OEPggp82RwigxNLL35LgT.jpeg
paper_authors.fullname: Pierre-Carl Langlais
paper_authors.type: user
paper_authors.user: Pclanglais
paper_authors._id: 680fa50d618d5374f2c39caa
paper_authors.name: Pavel Chizhov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:42:52.583Z
paper_authors._id: 660b169b59f37491ddfb0dc8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/660b169b59f37491ddfb0dc8/YX9VF1kpp2G9FWWjGSggh.jpeg
paper_authors.fullname: Pavel Chizhov
paper_authors.type: user
paper_authors.user: pchizhov
paper_authors._id: 680fa50d618d5374f2c39cab
paper_authors.name: Mattia Nee
paper_authors._id: 680fa50d618d5374f2c39cac
paper_authors.name: Carlos Rosas Hinostroza
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-05T14:07:15.106Z
paper_authors._id: 65b3ddd382d38451342aca57
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65b3ddd382d38451342aca57/wYBAou-q38bKVROYd1YhN.jpeg
paper_authors.fullname: Carlos Rosas
paper_authors.type: user
paper_authors.user: carlosrosas
paper_authors._id: 680fa50d618d5374f2c39cad
paper_authors.name: Matthieu Delsart
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:43:10.648Z
paper_authors._id: 66f929ee94effc40bdde6cdb
paper_authors.avatarUrl: /avatars/1b2b9e69cc178d74e957bb43b309e152.svg
paper_authors.fullname: Matthieu Delsart
paper_authors.type: user
paper_authors.user: mdelsart
paper_authors._id: 680fa50d618d5374f2c39cae
paper_authors.name: Irène Girard
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:43:16.361Z
paper_authors._id: 660ef82427d13dc2ca52d152
paper_authors.avatarUrl: /avatars/8393c5c5a7630401e4a7627aefe2547a.svg
paper_authors.fullname: Irène Girard
paper_authors.type: user
paper_authors.user: Ip2j
paper_authors._id: 680fa50d618d5374f2c39caf
paper_authors.name: Othman Hicheur
paper_authors._id: 680fa50d618d5374f2c39cb0
paper_authors.name: Anastasia Stasenko
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:43:27.660Z
paper_authors._id: 654cb8411456ebd6f802db5b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eNUY-tTX5_EB3TKN3fJoE.jpeg
paper_authors.fullname: Anastasia Stasenko
paper_authors.type: user
paper_authors.user: anastasiastasenko
paper_authors._id: 680fa50d618d5374f2c39cb1
paper_authors.name: Ivan P. Yamshchikov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:43:33.420Z
paper_authors._id: 64a43264c641afb4689e83b6
paper_authors.avatarUrl: /avatars/336b629180c70b5fd3d49865c2ee0eb9.svg
paper_authors.fullname: Ivan P. Yamshchikov
paper_authors.type: user
paper_authors.user: kroniker
paper_title: Even Small Reasoners Should Quote Their Sources: Introducing the
    Pleias-RAG Model Family
paper_summary: 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.
paper_ai_summary: Two new mid-sized reasoning models, Pleias-RAG-350m and Pleias-RAG-1B, perform competitively on RAG benchmarks and support multilingual citation and grounding.
paper_ai_keywords: 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