Smarter Financial Search: Refining Document Understanding with AI

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Author: Denis Avetisyan

A new technique boosts the accuracy of information retrieval from complex financial filings by leveraging large language models to improve semantic search.

Performance metrics, quantified with standard errors, demonstrate evaluation results on the FinanceBench dataset.
Performance metrics, quantified with standard errors, demonstrate evaluation results on the FinanceBench dataset.

This research presents a method for adapting embedding models to financial documents through iterative contrastive learning and LLM-based distillation.

Despite advances in large language models, applying them to specialized domains like finance is hindered by computational cost and the need for precise information retrieval. This paper, ‘Adaptation of Embedding Models to Financial Filings via LLM Distillation’, introduces a scalable pipeline for training specialized embedding models from unlabeled financial filings, leveraging a general-purpose model and LLM-judged relevance. Our method achieves significant improvements-up to 44.6%-in retrieval accuracy across various filing types, bridging the gap between general-purpose and domain-specific models without costly human annotation. Could this iterative distillation approach unlock more efficient and accurate knowledge access in other complex, data-rich fields?

The Challenge of Semantic Understanding in Finance

Conventional keyword-based search systems frequently falter when applied to the financial domain, primarily because of the specialized and often ambiguous language inherent in financial texts. These systems operate by matching exact terms, failing to grasp the contextual meaning behind phrases or the relationships between concepts. For example, a search for “credit risk” might retrieve documents mentioning both positive credit ratings and impending defaults, overlooking the critical distinction. This limitation stems from the inability to discern synonyms, understand negations, or interpret the subtle implications of industry jargon. Consequently, vital information can remain hidden within a vast corpus of financial data, hindering effective analysis and decision-making. The nuances of financial language-including complex sentence structures and the reliance on implied meanings-pose a significant challenge to systems designed for simple lexical matching.

The proliferation of financial data, ranging from dense regulatory filings and complex derivative contracts to rapidly updating market news and analyst reports, has created an unprecedented challenge for information retrieval systems. Traditional methods, reliant on simple keyword matching, are increasingly inadequate when confronted with the intricate language and nested clauses characteristic of financial documentation. This complexity isn’t merely a matter of volume; it’s a qualitative shift demanding techniques capable of understanding the meaning behind the words, not just their presence. Consequently, sophisticated approaches-like natural language processing and machine learning-are now essential to effectively extract relevant insights from the ever-growing corpus of financial data, enabling professionals to navigate information overload and make informed decisions.

The financial sector is rapidly adopting semantic search technologies to address growing demands for efficiency and accuracy in critical operations. Automated compliance, for instance, benefits from the ability to interpret regulatory text not just by keywords, but by understanding the underlying meaning and intent, ensuring comprehensive adherence and reducing the risk of penalties. Similarly, risk assessment is significantly enhanced, as semantic search can identify subtle connections and patterns within vast datasets of financial reports, news articles, and market data – information often missed by traditional methods. Perhaps most visibly, client query resolution is becoming increasingly streamlined; semantic search allows systems to comprehend the context of a question, delivering precise and relevant answers far beyond simple keyword matching, ultimately improving client satisfaction and freeing up valuable human resources.

Constructing a Domain-Specific Embedding Model

The training dataset for the retrieval embedding model was generated using an Open-Weights Large Language Model (LLM). This approach enabled the creation of a significantly larger and more diverse dataset than manual annotation would allow. The LLM was prompted to produce synthetic data exhibiting semantic relationships relevant to financial text, effectively scaling the training process. This method prioritizes data quality through controlled generation, surpassing limitations inherent in relying solely on publicly available datasets or manual labeling efforts. The resulting synthetic data was then used to fine-tune the embedding model, improving its ability to accurately represent semantic similarity within the target domain.

The Retrieval Embedding Model utilizes a fine-tuning process applied to a pre-trained language model, optimizing it for semantic understanding within the financial domain. This fine-tuning focuses the model’s vector representations – the embeddings – to accurately reflect the relationships between financial concepts, entities, and terminology. The resultant embeddings are designed to minimize the distance between vectors representing semantically similar financial text and maximize the distance between those representing dissimilar content. This enables efficient retrieval of relevant financial documents and improved performance in downstream tasks such as question answering and financial analysis.

The training dataset consists of 2.52 million triples used to fine-tune the embedding model; 950,000 of these triples were specifically reserved for validation purposes to assess model performance and prevent overfitting. Data preparation included the creation of both positive and negative examples within these triples, enabling the model to learn distinctions between semantically similar and dissimilar financial concepts. This curated dataset provides a robust foundation for learning relevant semantic relationships within the target domain.

t-SNE visualizations demonstrate that incorporating positive example mining yields a more diverse training set, as evidenced by the broader distribution of difference vectors compared to the baseline.
t-SNE visualizations demonstrate that incorporating positive example mining yields a more diverse training set, as evidenced by the broader distribution of difference vectors compared to the baseline.

Refining Retrieval Through Contrastive Learning

Triplet Loss is employed as the training objective to optimize the embedding space for information retrieval. This loss function operates by minimizing the distance between embeddings of relevant query-document pairs, thereby increasing their similarity score. Simultaneously, it maximizes the distance between embeddings of irrelevant query-document pairs, decreasing their similarity score. The loss is calculated based on the relative distances: the distance between the relevant pair must be smaller than the distance between the irrelevant pair by a defined margin. Formally, the triplet loss can be represented as $L = max(0, d(q,p) – d(q,n) + \alpha)$, where $d$ is a distance metric, $q$ is the query, $p$ is the positive (relevant) document, $n$ is the negative (irrelevant) document, and $\alpha$ is the margin.

To enhance the training dataset for contrastive learning, the model employs InPars for positive example augmentation. This technique generates approximately 103 positive passages for each query, significantly increasing the scale and diversity of relevant examples used during training. By leveraging InPars, the model avoids reliance on a limited set of manually curated positive examples and instead benefits from a more comprehensive and representative sample of potentially relevant documents, ultimately improving retrieval performance and generalization capabilities.

t-distributed Stochastic Neighbor Embedding (t-SNE) was employed as a dimensionality reduction technique to visualize the high-dimensional embedding space generated for queries and documents. This process reduces the number of dimensions while preserving the relative distances between data points, allowing for a two or three-dimensional representation suitable for plotting. Visual inspection of the resulting t-SNE plots confirmed the model’s capacity to cluster semantically similar queries and documents in close proximity, indicating successful embedding of semantic relationships. Specifically, relevant document-query pairs consistently appeared clustered together, while irrelevant pairs were well-separated, providing qualitative validation of the model’s retrieval performance.

Beyond Simple Retrieval: Expanding Performance Horizons

The foundation of many advanced financial applications now rests upon a specifically fine-tuned Retrieval Embedding Model, acting as the core of Retrieval-Augmented Generation (RAG) systems. These models don’t simply know information; they expertly locate relevant data from vast financial documents – reports, news articles, regulatory filings – to inform their responses and decision-making. This process bypasses the limitations of a model’s pre-existing knowledge, allowing it to dynamically access and integrate current, precise information. By converting financial text into numerical representations – embeddings – the model can quickly identify the most pertinent content based on a user’s query, significantly improving the accuracy and reliability of financial analysis, risk assessment, and automated reporting. This represents a shift from static knowledge to a dynamic, information-retrieval-powered intelligence.

The Retrieval Embedding Model demonstrates a substantial leap in information retrieval accuracy within financial applications, achieving a Recall@1 score of 62.8%. This metric indicates that, when presented with a query, the model successfully retrieves the most relevant document from a dataset over 62% of the time. Importantly, this performance represents a marked improvement over OpenAI’s leading general-purpose embedding models, which currently achieve a Recall@1 of 39.2% on the same tasks. The nearly 24-point gain underscores the model’s specialized training and optimization for the nuances of financial data, enabling more precise and reliable information access for downstream applications like automated reporting and investment analysis. This enhanced recall directly translates to fewer missed insights and more effective decision-making within complex financial scenarios.

Retrieval accuracy in financial applications can be substantially improved by moving beyond simple vector similarity searches and incorporating knowledge graphs through a technique called GraphRAG. This approach represents a shift towards contextually aware retrieval, where relationships between financial entities – companies, markets, regulations – are explicitly modeled and utilized during the search process. Rather than solely relying on keyword matching or semantic similarity, GraphRAG allows the system to understand how different pieces of information relate to each other, enabling it to surface more relevant and nuanced results. By traversing these interconnected knowledge graphs, the system can identify indirect connections and infer relationships that would be missed by traditional methods, ultimately leading to more informed decision-making and improved performance in complex financial tasks. The integration offers a pathway to unlock deeper insights from financial data, paving the way for more sophisticated and reliable retrieval-augmented generation systems.

The Retrieval Embedding Model’s utility extends beyond simple information recall, proving instrumental in the development of sophisticated Agentic Models designed for financial applications. These agents are not merely passive responders; they leverage the model to actively plan and execute complex tasks. By retrieving pertinent financial data, the model equips the agent with the necessary context to formulate strategies, assess risks, and make informed decisions – effectively simulating the reasoning of a financial analyst. This capability allows for automation of tasks previously requiring human expertise, such as portfolio optimization, fraud detection, and personalized financial advice, all driven by the model’s ability to provide accurate and relevant information at each stage of the agent’s decision-making process.

The pursuit of effective financial document analysis, as detailed in this work, echoes a fundamental tenet of elegant system design. It prioritizes distillation – removing extraneous noise to reveal the core signal. As Marvin Minsky observed, “The more definitions or axioms you have, the more complicated the system is, and the harder it is to understand.” This paper embodies that principle by iteratively refining the embedding model through contrasting examples, effectively stripping away irrelevant information to enhance retrieval accuracy. The focus on positive and negative example mining isn’t about adding complexity, but about subtracting ambiguity, leading to a more concise and powerful system for accessing critical financial data. The result is a retrieval mechanism that approaches clarity, offering efficient and relevant information access.

What Lies Ahead?

This work clarifies a practical path for embedding model adaptation. Yet, relevance remains elusive. Simple gains from contrastive learning plateau. The current reliance on positive/negative example mining feels… iterative, not innovative. Abstractions age, principles don’t. The true challenge isn’t finding more data, but defining what constitutes genuine financial insight.

Future work must address the fragility of these models. Domain shifts, even subtle ones, reveal underlying instability. Robustness isn’t achieved through larger models, but through a deeper understanding of financial language itself. Every complexity needs an alibi. A focus on explainability-tracing the model’s reasoning-is paramount.

Ultimately, the goal transcends mere retrieval. It’s about building systems capable of synthesizing information, identifying anomalies, and anticipating market movements. That requires moving beyond semantic search and embracing true financial intelligence. A difficult task, but worthwhile.

Original article: https://arxiv.org/pdf/2512.08088.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-10 16:04