title: The Aloe Family Recipe for Open and Specialized Healthcare LLMs
summary: Purpose: With advancements in Large Language Models (LLMs) for healthcare,
  the need arises for competitive open-source models to protect the public
  interest. This work contributes to the field of open medical LLMs by optimizing
  key stages of data preprocessing and training, while showing how to improve
  model safety (through DPO) and efficacy (through RAG). The evaluation
  methodology used, which includes four different types of tests, defines a new
  standard for the field. The resultant models, shown to be competitive with the
  best private alternatives, are released with a permisive license.
    Methods: Building on top of strong base models like Llama 3.1 and Qwen 2.5,
  Aloe Beta uses a custom dataset to enhance public data with synthetic Chain of
  Thought examples. The models undergo alignment with Direct Preference
  Optimization, emphasizing ethical and policy-aligned performance in the
  presence of jailbreaking attacks. Evaluation includes close-ended, open-ended,
  safety and human assessments, to maximize the reliability of results.
    Results: Recommendations are made across the entire pipeline, backed by the
  solid performance of the Aloe Family. These models deliver competitive
  performance across healthcare benchmarks and medical fields, and are often
  preferred by healthcare professionals. On bias and toxicity, the Aloe Beta
  models significantly improve safety, showing resilience to unseen jailbreaking
  attacks. For a responsible release, a detailed risk assessment specific to
  healthcare is attached to the Aloe Family models.
    Conclusion: The Aloe Beta models, and the recipe that leads to them, are a
  significant contribution to the open-source medical LLM field, offering
  top-of-the-line performance while maintaining high ethical requirements. This
  work sets a new standard for developing and reporting aligned LLMs in
  healthcare.
paper_authors._id: 682d83494c4685831f85ec92
paper_authors.name: Dario Garcia-Gasulla
paper_authors._id: 682d83494c4685831f85ec93
paper_authors.name: Jordi Bayarri-Planas
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-21T07:39:54.713Z
paper_authors._id: 661e8559b0338c03bd6e5054
paper_authors.avatarUrl: /avatars/044d69afa40ddef4485aebfe984da96b.svg
paper_authors.fullname: Bayarri
paper_authors.type: user
paper_authors.user: JordiBayarri-bsc
paper_authors._id: 682d83494c4685831f85ec94
paper_authors.name: Ashwin Kumar Gururajan
paper_authors._id: 682d83494c4685831f85ec95
paper_authors.name: Enrique Lopez-Cuena
paper_authors._id: 682d83494c4685831f85ec96
paper_authors.name: Adrian Tormos
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-21T11:04:23.232Z
paper_authors._id: 6622352d9fcf61dee8a1f24d
paper_authors.avatarUrl: /avatars/dd164c50b3ecb8861e2294337b942e6f.svg
paper_authors.fullname: Adrian Tormos
paper_authors.type: user
paper_authors.user: adriantormos
paper_authors._id: 682d83494c4685831f85ec97
paper_authors.name: Daniel Hinjos
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T08:39:40.380Z
paper_authors._id: 65b8e66f078c543033e49672
paper_authors.avatarUrl: /avatars/aa81fd59b4ec624245a4a84d2708962d.svg
paper_authors.fullname: Daniel Hinjos García
paper_authors.type: user
paper_authors.user: danihinjos
paper_authors._id: 682d83494c4685831f85ec98
paper_authors.name: Pablo Bernabeu-Perez
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-21T07:39:54.713Z
paper_authors._id: 620683e7eeb1b73d904c96e5
paper_authors.avatarUrl: /avatars/d0309ac9408530a74f1799e175cc5fad.svg
paper_authors.fullname: Pablo Bernabeu
paper_authors.type: user
paper_authors.user: pabberpe
paper_authors._id: 682d83494c4685831f85ec99
paper_authors.name: Anna Arias-Duart
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-21T07:59:53.756Z
paper_authors._id: 65d71603ca16ef9ba7fb2efb
paper_authors.avatarUrl: /avatars/7d3e1436427f7f58c86fb1f8724c4244.svg
paper_authors.fullname: Anna
paper_authors.type: user
paper_authors.user: annariasdu
paper_authors._id: 682d83494c4685831f85ec9a
paper_authors.name: Pablo Agustin Martin-Torres
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T09:03:22.597Z
paper_authors._id: 6565c2a4131d13ccc5df1435
paper_authors.avatarUrl: /avatars/218ceac504772e7f0fb3ee4d46d4fab7.svg
paper_authors.fullname: Pablo Agustin Martin Torres
paper_authors.type: user
paper_authors.user: PabloMartinTorres
paper_authors._id: 682d83494c4685831f85ec9b
paper_authors.name: Marta Gonzalez-Mallo
paper_authors._id: 682d83494c4685831f85ec9c
paper_authors.name: Sergio Alvarez-Napagao
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-21T11:38:27.934Z
paper_authors._id: 62d16c742ad06bbc89217797
paper_authors.avatarUrl: /avatars/11ce629fbcb33f2431164d8a3e54c876.svg
paper_authors.fullname: Sergio Alvarez-Napagao
paper_authors.type: user
paper_authors.user: tranchis
paper_authors._id: 682d83494c4685831f85ec9d
paper_authors.name: Eduard Ayguadé-Parra
paper_authors._id: 682d83494c4685831f85ec9e
paper_authors.name: Ulises Cortés
paper_title: The Aloe Family Recipe for Open and Specialized Healthcare LLMs
paper_summary: Purpose: With advancements in Large Language Models (LLMs) for healthcare,
  the need arises for competitive open-source models to protect the public
  interest. This work contributes to the field of open medical LLMs by optimizing
  key stages of data preprocessing and training, while showing how to improve
  model safety (through DPO) and efficacy (through RAG). The evaluation
  methodology used, which includes four different types of tests, defines a new
  standard for the field. The resultant models, shown to be competitive with the
  best private alternatives, are released with a permisive license.
    Methods: Building on top of strong base models like Llama 3.1 and Qwen 2.5,
  Aloe Beta uses a custom dataset to enhance public data with synthetic Chain of
  Thought examples. The models undergo alignment with Direct Preference
  Optimization, emphasizing ethical and policy-aligned performance in the
  presence of jailbreaking attacks. Evaluation includes close-ended, open-ended,
  safety and human assessments, to maximize the reliability of results.
    Results: Recommendations are made across the entire pipeline, backed by the
  solid performance of the Aloe Family. These models deliver competitive
  performance across healthcare benchmarks and medical fields, and are often
  preferred by healthcare professionals. On bias and toxicity, the Aloe Beta
  models significantly improve safety, showing resilience to unseen jailbreaking
  attacks. For a responsible release, a detailed risk assessment specific to
  healthcare is attached to the Aloe Family models.
    Conclusion: The Aloe Beta models, and the recipe that leads to them, are a
  significant contribution to the open-source medical LLM field, offering
  top-of-the-line performance while maintaining high ethical requirements. This
  work sets a new standard for developing and reporting aligned LLMs in
  healthcare.
paper_ai_summary: Aloe Beta models improve open-source medical Large Language Models through enhanced preprocessing, Direct Preference Optimization, Retrieval-Augmented Generation, and a rigorous evaluation methodology, achieving competitive performance and high safety standards.
paper_ai_keywords: Large Language Models LLMs Direct Preference Optimization DPO Retrieval-Augmented Generation RAG Chain of Thought examples jailbreaking attacks safety assessments human assessments