Beyond Search: Smarter Retrieval for Financial AI

by

Author: Denis Avetisyan

New research reveals that advanced retrieval methods, including refined search and reranking techniques, significantly improve the accuracy of large language models when applied to complex financial document question answering.

The system employs a retrieval strategy that first identifies a relevant text chunk via vector search, then strategically broadens the contextual scope by incorporating neighboring chunks-specifically, the two preceding and following chunks-before presenting this expanded context to a language model for response generation.
The system employs a retrieval strategy that first identifies a relevant text chunk via vector search, then strategically broadens the contextual scope by incorporating neighboring chunks-specifically, the two preceding and following chunks-before presenting this expanded context to a language model for response generation.

Vector-based retrieval augmented generation, enhanced with cross-encoder reranking and hierarchical retrieval strategies, outperforms node-based reasoning systems for financial data analysis.

Despite advancements in knowledge-intensive tasks, effectively leveraging unstructured financial data remains a challenge for Large Language Models. This is addressed in ‘Rethinking Retrieval: From Traditional Retrieval Augmented Generation to Agentic and Non-Vector Reasoning Systems in the Financial Domain for Large Language Models’, which systematically compares vector-based and hierarchical reasoning approaches for question answering over SEC filings. Our findings demonstrate that enhanced vector-based Retrieval-Augmented Generation-utilizing techniques like cross-encoder reranking and optimized chunk retrieval-significantly outperforms node-based systems in both accuracy and answer quality with comparable latency. How can these insights inform the development of more robust and cost-effective financial information retrieval systems for increasingly complex analytical tasks?

Beyond Simple Search: The Limits of Conventional Knowledge Retrieval

Contemporary Retrieval-Augmented Generation (RAG) systems frequently lean on dense vector embeddings to represent and retrieve information, yet this approach presents inherent limitations in grasping subtle meanings and facilitating complex logical steps. While effective for identifying documents with similar keywords, these embeddings often fail to capture the nuances of language, such as irony, metaphor, or context-dependent definitions. The process transforms text into numerical vectors, inevitably losing some of the original semantic richness and relational information; consequently, the system may struggle with questions requiring inference, common-sense reasoning, or the integration of knowledge from multiple sources within a document. This reliance on vector similarity, while computationally efficient, can therefore result in retrieved passages that are superficially relevant but lack the depth or precision needed to generate truly insightful or accurate responses.

Retrieval-Augmented Generation (RAG) systems, while powerful, are increasingly challenged by the ‘Lost in the Middle’ phenomenon when processing lengthy documents. This limitation arises from the fixed size of the ‘Context Window’ – the amount of text a model can effectively analyze at once. Information presented at the beginning or end of a document tends to receive greater attention, while crucial details embedded within the middle sections often go overlooked or are inadequately utilized during knowledge retrieval. Consequently, responses generated from these systems may be incomplete or inaccurate, as they fail to synthesize information distributed across the entire document, highlighting a critical need for methods that enhance attention to, and retention of, centrally located data.

Traditional knowledge retrieval methods, including both simple keyword searches and increasingly common dense vector embeddings, often fall short when faced with complex information needs. While keyword searches struggle with synonymy and semantic understanding – failing to connect related concepts expressed with different wording – dense embeddings, despite capturing some semantic meaning, can miss subtle but critical relationships between ideas. This limitation arises because embeddings represent words and phrases as points in a high-dimensional space, and the distance between these points doesn’t always accurately reflect the nuanced connections crucial for comprehensive knowledge retrieval. Consequently, systems relying solely on these methods may retrieve relevant but incomplete information, or conversely, fail to identify truly relevant content hidden behind different phrasing or implied connections, ultimately hindering effective reasoning and accurate generation.

Structuring Knowledge: A Hierarchical Approach to Retrieval

Hierarchical Node-Based Reasoning utilizes a document organization strategy distinct from methods relying on dense vector embeddings. This approach constructs a ‘Node Tree’ representation of each document, explicitly modeling its inherent structural components – such as chapters, sections, and subsections. Rather than representing the entire document as a single vector, the method decomposes it into discrete nodes connected by hierarchical relationships. This allows for targeted retrieval based on the document’s structure, focusing computational resources on potentially relevant portions and avoiding the need to process the complete document content as a single unit. The resulting node tree serves as a navigable map of the document’s information architecture, facilitating a more efficient and focused search process.

Traversal-based retrieval utilizes pre-existing structural cues within documents, such as tables of contents, headings, and subheadings, to navigate and extract relevant information. Instead of processing entire documents, the system identifies and accesses specific sections and subsections indicated by these cues. This method allows for targeted information retrieval by following a hierarchical path based on the user’s query, effectively focusing the search on logically related content. The process bypasses the need to analyze unstructured text by prioritizing document structure, enabling efficient access to pertinent details within complex or lengthy documents.

Current large language models (LLMs) are constrained by fixed context window sizes, limiting the amount of text they can process at once. Hierarchical Node-Based Reasoning mitigates this limitation by retrieving only the specific, structurally-defined segments of a document relevant to a query. Instead of processing entire documents, the system navigates a pre-built node tree – derived from document structure like a table of contents – and retrieves only the pertinent nodes. This targeted retrieval of smaller, coherent text segments allows the LLM to focus on information within its context window, effectively expanding the amount of information accessible for reasoning without exceeding model limitations. The method prioritizes structural coherence, ensuring retrieved segments maintain internal consistency and relevance to the overall document hierarchy.

Precision Through Combination: Hybrid Search and Contextual Enrichment

Vector-Based Agentic Retrieval Augmented Generation (RAG) systems employ a ‘Hybrid Search’ approach to initial document retrieval, integrating both semantic and lexical matching techniques. Semantic search utilizes vector embeddings to identify documents conceptually similar to the query, while lexical matching, such as Boolean or keyword searches, focuses on direct term matches. This combination aims to capture a broader range of relevant documents than either method alone. Following the initial retrieval, ‘Metadata Filtering’ is applied to further refine the results by incorporating attributes such as date, source, or category, allowing the system to prioritize and return only the most pertinent information based on defined criteria.

Small-to-Big Retrieval is a technique used to improve the contextual relevance of retrieved information by expanding initial query results. This method operates by supplementing the initially retrieved content chunks with surrounding, neighboring text segments. Evaluations demonstrate a 65% improvement in performance, measured as a win rate against baseline chunking methods, while incurring only 0.2 seconds of additional latency. The purpose of this expansion is to address potential information loss that can occur when relying solely on isolated chunks, thereby enhancing the overall quality and completeness of the context provided to the language model.

Cross-Encoder Reranking improves retrieval precision by reordering initially retrieved text chunks based on a more detailed assessment of relevance to the query. This process moves the most pertinent information to the top of the result set, leading to a demonstrated 59% absolute increase in Mean Reciprocal Rank (MRR), improving the score from 0.160 to 0.750. Furthermore, implementation of Cross-Encoder Reranking achieved perfect Recall@5, indicating that 100% of relevant documents were present within the top five retrieved results.

Measuring Success: Rigorous Evaluation of Knowledge Retrieval

Evaluating the effectiveness of information retrieval systems necessitates quantifiable metrics, and established standards like Mean Reciprocal Rank (MRR) and Recall@5 play a crucial role in this process. MRR assesses the average rank of the first relevant document retrieved for a query, providing insight into precision; a higher MRR indicates that relevant information appears earlier in the results. Complementing this, Recall@5 measures the proportion of relevant documents retrieved within the top five results, focusing on completeness. By calculating these metrics, researchers can objectively compare different retrieval strategies, identifying systems that not only find relevant information quickly but also ensure a high percentage of all relevant documents are successfully retrieved, ultimately leading to more effective and reliable knowledge access.

The evaluation of large language model (LLM) outputs often requires human assessment, a process prone to subjectivity and scalability issues. To address this, researchers are increasingly utilizing ‘LLM-as-a-Judge’, a paradigm where another LLM is employed to automatically score the quality of generated answers. This approach offers a robust and consistent method for evaluating responses, bypassing the limitations of manual grading. By leveraging the inherent reasoning capabilities of these models, ‘LLM-as-a-Judge’ can assess factors like relevance, coherence, and factual accuracy, providing objective scores that correlate strongly with human judgment. The automated nature of this evaluation is particularly valuable for iterative model development and benchmarking, allowing for rapid and scalable assessment of performance improvements and facilitating more reliable comparisons between different approaches to question answering and information retrieval.

Evaluations reveal a significant performance advantage for vector-based agentic Retrieval-Augmented Generation (RAG) systems in the domain of financial document question answering; these systems achieved a 68% win rate when compared to traditional hierarchical node-based approaches. This improvement in retrieval precision and answer quality was attained without sacrificing speed, as the vector-based RAG exhibited comparable latency – averaging 5.2 seconds – against the 5.98 seconds recorded for hierarchical node-based systems. The results suggest that leveraging vector embeddings and an agentic approach enhances the ability to effectively navigate and synthesize information from complex financial documents, offering a more robust and efficient solution for knowledge retrieval and question answering tasks.

The pursuit of effective information retrieval, as detailed in this exploration of Retrieval-Augmented Generation systems, often introduces layers of complexity that obscure fundamental understanding. This work demonstrates a preference for streamlined methodologies – vector-based RAG, fortified by techniques like cross-encoder reranking, proving more effective than intricate hierarchical reasoning for financial question answering. Alan Turing observed, “Sometimes people who are unhappy tend to look at the world as if there were nothing else.” Similarly, excessive complication in system design-introducing unnecessary nodes or layers-can obscure the core objective: accurate and efficient knowledge retrieval. The focus on demonstrable performance, prioritizing clarity over architectural grandeur, aligns with a principle of parsimony; a system’s value lies not in its intricacy, but in its ability to deliver reliable results.

Future Directions

The demonstrated efficacy of optimized vector-based retrieval, even exceeding hierarchical approaches, does not signify a terminus. It highlights a fundamental asymmetry: current evaluation metrics prioritize answer correctness, a necessary but insufficient condition. The true cost remains obscured – the computational burden of increasingly expansive vector spaces, and the implicit biases encoded within the indexed corpus. Future work must rigorously quantify this ‘cost of knowing’, shifting from accuracy alone to a holistic assessment encompassing latency, memory footprint, and demonstrable fairness.

The persistence of error, even with sophisticated reranking, suggests a deeper limitation. Current systems excel at finding relevant information, but struggle with synthesizing nuanced understanding. The financial domain, inherently adversarial, demands not merely fact retrieval, but the ability to discern intent, identify fallacies, and extrapolate from incomplete data. This necessitates a move beyond semantic similarity, towards systems capable of abductive reasoning – inferring the most plausible explanation, given the evidence.

Ultimately, the pursuit of ‘intelligence’ in these systems is a misdirection. The goal is not to replicate human cognition, but to create tools that augment it. The most fruitful path lies not in ever-larger models, but in rigorously defined interfaces – mechanisms for transparently conveying uncertainty, explicitly highlighting data provenance, and enabling human oversight. Unnecessary complexity is violence against attention; a truly intelligent system knows when to remain silent.

Original article: https://arxiv.org/pdf/2511.18177.pdf

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

See also:

2025-11-25 15:54