Searching for Answers: The Challenge of Financial Information Retrieval

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Author: Denis Avetisyan

New research reveals current systems struggle with complex questions requiring synthesis from multiple financial documents.

A query generation pipeline leverages both single- and multi-document approaches—the latter employing topic-based merging, context deepening, and comparative analysis—to produce inquiries subsequently vetted for quality and demonstrable answerability.
A query generation pipeline leverages both single- and multi-document approaches—the latter employing topic-based merging, context deepening, and comparative analysis—to produce inquiries subsequently vetted for quality and demonstrable answerability.

This paper introduces KoBankIR, a Korean-language benchmark dataset, and demonstrates limitations in existing retrieval models, advocating for improved reasoning-augmented evaluation techniques.

Despite advancements in information retrieval, evaluating systems within specialized financial domains remains challenging due to a scarcity of representative benchmarks and legal restrictions on accessing real-world data. This paper, ‘Query Generation Pipeline with Enhanced Answerability Assessment for Financial Information Retrieval’, introduces a systematic methodology leveraging large language models to construct a Korean-language benchmark, KoBankIR, comprising complex, multi-document queries derived from official banking documents. Experiments reveal that existing retrieval models struggle with these queries, demonstrating a significant gap in performance and highlighting the need for more robust techniques. Can this approach facilitate the development of truly effective financial information retrieval systems capable of navigating complex, real-world banking scenarios?

The Limits of Keyword-Driven Search

Traditional information retrieval systems struggle with queries requiring synthesis across multiple documents. They often prioritize keyword identification, an inadequate approach for tasks demanding contextual understanding and relational reasoning. This limitation is particularly pronounced in specialized domains like finance, where accurate information aggregation is crucial for effective decision-making. The inability to synthesize information hinders both quantitative analysis and qualitative insight. The financial domain presents a unique challenge due to the complexity of documentation and the critical need for precision; effective retrieval requires understanding hierarchical relationships within reports, filings, and analyses. The architecture of information, like any system, dictates the flow and utility of its components.

The financial document dataset exhibits a hierarchical structure, indicating nested relationships between different document components.
The financial document dataset exhibits a hierarchical structure, indicating nested relationships between different document components.

Constructing Queries That Demand Synthesis

The proposed solution employs a query generation pipeline designed to evolve from simple requests to complex, multi-document inquiries. This pipeline automates the creation of questions that require synthesis, rather than mere retrieval. Key techniques include Context Deepening, which expands queries with relevant background information, and Comparing and Contrasting, which actively seeks inter-document relationships. Topic-Based Merging consolidates information from multiple documents, formulating unified queries that necessitate genuine information integration.

Reasoning as the Standard for Evaluation

Current automatic evaluation metrics for question generation often rely on superficial relevance scoring. To address this, a Reasoning-Augmented Evaluator was developed to more accurately assess query answerability. This approach models the cognitive process of determining whether a question can be answered from provided information. The evaluator uses a ‘think’ step, powered by the DeepSeek-R1-Distill-Qwen model, to simulate the reasoning required to answer the query. Evaluation on the KoBankIR benchmark demonstrated a Pearson correlation coefficient of 0.60 between the evaluator and human judgments, validating its effectiveness.

The reasoning-augmented evaluation method involves a process of integrating reasoning steps to improve assessment accuracy.
The reasoning-augmented evaluation method involves a process of integrating reasoning steps to improve assessment accuracy.

Performance and the Pursuit of Intelligent Retrieval

Evaluations demonstrate that the proposed query generation pipeline, when integrated with dense retrieval methods like GTE-Qwen2-1.5B-instruct and Multilingual-e5-Large, surpasses traditional sparse retrieval techniques. A hybrid approach achieved a peak NDCG@5 of 0.6795, indicating substantial improvement. Quantitative assessment revealed a mean Average Precision at 5 (mAP@5) of 0.6167 and a Recall at 10 of 0.8663. The top-performing single model, BGE-M3 (Dense), attained an NDCG@5 of 0.6452. This advancement carries significant implications for developing more intelligent and effective information retrieval systems, particularly within complex domains. Systems break along invisible boundaries—understanding these boundaries is critical.

The pursuit of effective financial information retrieval, as detailed in this study, demands a holistic understanding of system architecture. Each component, from query generation to answerability assessment, influences the overall performance. This echoes Alan Turing’s sentiment: “Sometimes people who are untutored are more perceptive than those who have learned too much.” The KoBankIR benchmark reveals current models struggle with complex queries, demonstrating that even sophisticated algorithms can falter without a foundational grasp of nuanced information needs. A seemingly simple query generation pipeline, when viewed within the larger system of multi-document retrieval and reasoning, reveals inherent trade-offs; streamlining one aspect can inadvertently diminish the system’s capacity for comprehensive understanding. The structure, therefore, dictates the system’s behavior and its ultimate effectiveness.

What’s Next?

The introduction of KoBankIR serves as a necessary perturbation to the field. Existing benchmarks, it appears, have largely masked the inherent fragility of information retrieval systems when confronted with genuinely complex queries—those demanding synthesis across multiple documents. The observed performance limitations aren’t simply a matter of scaling model parameters; they suggest a fundamental disconnect between current approaches and the nuanced reasoning required for effective financial information access. The system, predictably, falters when forced to interpret rather than merely locate.

Future work must move beyond treating retrieval as a purely lexical exercise. A shift towards models that explicitly represent document structure and relationships – a skeletal framework upon which meaning can be built – seems essential. The challenge lies not in achieving higher precision on isolated facts, but in fostering a system’s capacity to trace the logical connections between them. Simply adding more data will not resolve the underlying architectural shortcomings.

Ultimately, the true measure of progress will not be benchmark scores, but the emergence of systems capable of handling ambiguity and uncertainty—systems that, like any robust organism, can maintain integrity even when presented with incomplete or contradictory information. The pursuit of ever-larger models feels increasingly like rearranging deck chairs; a focus on elegant, structurally sound foundations is now paramount.

Original article: https://arxiv.org/pdf/2511.05000.pdf

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

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2025-11-10 17:30