Demystifying Domain-adaptive Post-training for Financial LLMs
Authors: name: Zixuan Ke, name: Yifei Ming, name: Xuan-Phi Nguyen, name: Caiming Xiong, name: Shafiq Joty
Organization: Salesforce AI Research

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