Demystifying Domain-adaptive Post-training for Financial LLMs
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
67842eede11224514e7c86df
Zixuan Ke
claimed_verified
2025-01-13T08:54:34.266Z
63eea3f9bfe4ead22ca5577b
/avatars/39c7ac4c8036c2fe37e68d20f6864985.svg
Zixuan Ke
user
ZixuanKe
67842eede11224514e7c86e0
Yifei Ming
admin_assigned
2025-01-13T21:04:39.730Z
641787ad1f1f3b0fa80f5340
/avatars/489b7c999bce2160e27c9b487e1026ef.svg
yifei ming
user
alvinming
67842eede11224514e7c86e1
Xuan-Phi Nguyen
67842eede11224514e7c86e2
Caiming Xiong
admin_assigned
2025-01-13T21:04:55.286Z
649dbcc4e0fff1ed099dc80a
/avatars/c87c273ca628dbcddccbf1ee19b2ce33.svg
Caiming Xiong
user
cxiong
67842eede11224514e7c86e3
Shafiq Joty
Demystifying Domain-adaptive Post-training for Financial LLMs
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
FINDAP, a systematic investigation, improves LLMs for finance through continual pretraining, instruction tuning, and preference alignment, achieving state-of-the-art performance using a novel preference data distillation method.
FINDAP post-training large language models LLMs domain adaptation continual pretraining instruction tuning preference alignment preference data distillation generative reward model Llama-Fin financial tasks