Adaptation of Embedding Models to Financial Filings via LLM Distillation

TL;DR

This paper presents a scalable, cost-effective pipeline for adapting general retrieval embedding models to the financial domain by distilling domain knowledge through iterative training with LLM-judged relevance, significantly improving retrieval performance.

Contribution

It introduces an iterative distillation method that leverages LLM relevance judgments to enhance financial retrieval embeddings without extensive human annotation.

Findings

27.7% improvement in MRR@5

44.6% improvement in mean DCG@5

Improved NDCG on 3 of 4 document classes in FinanceBench

Abstract

Despite advances in generative large language models (LLMs), practical application of specialized conversational AI agents remains constrained by computation costs, latency requirements, and the need for precise domain-specific relevance measures. While existing embedding models address the first two constraints, they underperform on information retrieval in specialized domains like finance. This paper introduces a scalable pipeline that trains specialized models from an unlabeled corpus using a general purpose retrieval embedding model as foundation. Our method yields an average of 27.7% improvement in MRR$\texttt{@}$5, 44.6% improvement in mean DCG$\texttt{@}$5 across 14 financial filing types measured over 21,800 query-document pairs, and improved NDCG on 3 of 4 document classes in FinanceBench. We adapt retrieval embeddings (bi-encoder) for RAG, not LLM generators, using LLM-judged relevance to distill domain knowledge into a compact retriever. There are prior works which pair synthetically generated queries with real passages to directly fine-tune the retrieval model. Our pipeline differs from these by introducing interaction between student and teacher models that interleaves retrieval-based mining of hard positive/negative examples from the unlabeled corpus with iterative retraining of the student model's weights using these examples. Each retrieval iteration uses the refined student model to mine the corpus for progressively harder training examples for the subsequent training iteration. The methodology provides a cost-effective solution to bridging the gap between general-purpose models and specialized domains without requiring labor-intensive human annotation.