Demystifying Domain-adaptive Post-training for Financial LLMs
This work addresses the problem of adapting LLMs to specialized domains like finance for improved task performance, though it appears incremental as it builds on existing domain-adaptive post-training methods.
The paper tackles the challenge of domain-adaptive post-training for large language models in finance by introducing FINDAP, a systematic framework with components for defining capabilities, training recipes, datasets, and evaluation, resulting in the Llama-Fin model that achieves state-of-the-art performance across financial tasks.
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 consists of four key components: FinCap, which defines the core capabilities required for the target domain; FinRec, an effective training recipe that jointly optimizes continual pre-training and instruction-following, along with a novel preference data distillation method leveraging process signals from a generative reward model; FinTrain, a curated set of training datasets supporting FinRec; and FinEval, a comprehensive evaluation suite aligned with FinCap. 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