Automated Regulatory Compliance Question Answering in Financial Services with Domain-Adapted Retrieval-Augmented Generation
Replaces off-the-shelf dense or BM25 retrieval and prompted compact LLMs with a LegalBERT retriever tuned via entailment reformulation, contrastive in-batch negatives, and BM25 score fusion, plus optional RAFT-LoRA on a quantized 2B-12B generator, all feasible on a single 24 GB GPU. Costs include three retriever fine-tuning stages, labeled domain data for entailment and contrastive tuning, hybrid-fusion serving complexity, and per-domain RAFT adaptation. It can fail on domain transfer (no generalization to Australian case law), and RePASs gains do not demonstrate grounding: a closed-book model scored within 0.011 RePASs while citing nothing and misstating obligations, so a dedicated grounding evaluation must be built before production trust.
