title: Demystifying Domain-adaptive Post-training for Financial LLMs
summary: Domain-adaptive post-training of large language models (LLMs) has emerged as
  a promising approach for specialized domains such as medicine and finance.
  However, significant challenges remain in identifying optimal adaptation
  criteria and training strategies across varying data and model configurations.
  To address these challenges, we introduce FINDAP, a systematic and fine-grained
  investigation into domain-adaptive post-training of LLMs for the finance
  domain. Our approach begins by identifying the core capabilities required for
  the target domain and designing a comprehensive evaluation suite aligned with
  these needs. We then analyze the effectiveness of key post-training stages,
  including continual pretraining, instruction tuning, and preference alignment.
  Building on these insights, we propose an effective training recipe centered on
  a novel preference data distillation method, which leverages process signals
  from a generative reward model. The resulting model, Llama-Fin, achieves
  state-of-the-art performance across a wide range of financial tasks. Our
  analysis also highlights how each post-training stage contributes to distinct
  capabilities, uncovering specific challenges and effective solutions, providing
  valuable insights for domain adaptation of LLMs. Project page:
  https://github.com/SalesforceAIResearch/FinDap
paper_authors._id: 67842eede11224514e7c86df
paper_authors.name: Zixuan Ke
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-13T08:54:34.266Z
paper_authors._id: 63eea3f9bfe4ead22ca5577b
paper_authors.avatarUrl: /avatars/39c7ac4c8036c2fe37e68d20f6864985.svg
paper_authors.fullname: Zixuan Ke
paper_authors.type: user
paper_authors.user: ZixuanKe
paper_authors._id: 67842eede11224514e7c86e0
paper_authors.name: Yifei Ming
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-13T21:04:39.730Z
paper_authors._id: 641787ad1f1f3b0fa80f5340
paper_authors.avatarUrl: /avatars/489b7c999bce2160e27c9b487e1026ef.svg
paper_authors.fullname: yifei ming
paper_authors.type: user
paper_authors.user: alvinming
paper_authors._id: 67842eede11224514e7c86e1
paper_authors.name: Xuan-Phi Nguyen
paper_authors._id: 67842eede11224514e7c86e2
paper_authors.name: Caiming Xiong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-13T21:04:55.286Z
paper_authors._id: 649dbcc4e0fff1ed099dc80a
paper_authors.avatarUrl: /avatars/c87c273ca628dbcddccbf1ee19b2ce33.svg
paper_authors.fullname: Caiming Xiong
paper_authors.type: user
paper_authors.user: cxiong
paper_authors._id: 67842eede11224514e7c86e3
paper_authors.name: Shafiq Joty
paper_title: Demystifying Domain-adaptive Post-training for Financial LLMs
paper_summary: Domain-adaptive post-training of large language models (LLMs) has emerged as
  a promising approach for specialized domains such as medicine and finance.
  However, significant challenges remain in identifying optimal adaptation
  criteria and training strategies across varying data and model configurations.
  To address these challenges, we introduce FINDAP, a systematic and fine-grained
  investigation into domain-adaptive post-training of LLMs for the finance
  domain. Our approach begins by identifying the core capabilities required for
  the target domain and designing a comprehensive evaluation suite aligned with
  these needs. We then analyze the effectiveness of key post-training stages,
  including continual pretraining, instruction tuning, and preference alignment.
  Building on these insights, we propose an effective training recipe centered on
  a novel preference data distillation method, which leverages process signals
  from a generative reward model. The resulting model, Llama-Fin, achieves
  state-of-the-art performance across a wide range of financial tasks. Our
  analysis also highlights how each post-training stage contributes to distinct
  capabilities, uncovering specific challenges and effective solutions, providing
  valuable insights for domain adaptation of LLMs. Project page:
  https://github.com/SalesforceAIResearch/FinDap
paper_ai_keywords: domain-adaptive post-training large language models (LLMs) continual pretraining instruction tuning preference alignment preference data distillation generative reward model Llama-Fin state-of-the-art performance