KAHAN : Knowledge-Augmented Hierarchical Analysis and Narration for Financial Data Narration (2025)

Abstract: We propose KAHAN, a knowledge-augmented hierarchical framework that systematically extracts insights from raw tabular data at entity, pairwise, group, and system levels. KAHAN uniquely leverages LLMs as domain experts to drive the analysis. On DataTales financial reporting benchmark, KAHAN outperforms existing approaches by over 20% on narrative quality (GPT-4o), maintains 98.2% factuality, and demonstrates practical utility in human evaluation. Our results reveal that knowledge quality drives model performance through distillation, hierarchical analysis benefits vary with market complexity, and the framework transfers effectively to healthcare domains. The data and code are available at https://github.com/yajingyang/kahan.

## introduction data narration systems that convert structured data into natural language insights enable automated data interpretation @xcite . this capability is particularly valuable in financial markets, where analysts transform complex market data into narrative reports that interpret trends, compare performances, and contextualize movements for investment decisionmaking @xcite . while these financial narratives deliver critical value to stakeholders, their manual creation requires significant expertise and time, creating opportunities for automated approaches that bridge raw data and actionable insights. data narration systems face two key challenges. first, effective narratives require multilevel analysis-extracting insights at varying granularities and establishing connections between them. in financial contexts, this means analyzing individual assets, sector performance, and market-wide patterns simultaneously. second, meaningful narration demands domain knowledge augmentation. report date: 3/20/2023 stocks finish mixed as strength in technology led the nasdaq higher, but weakness in healthcare offset, with the s&p finishing little changed on the day, erasing strong gains. the nasdaq 100 rallied by 5.8%, posting its best performance since november, despite a slump on friday. as investors reassessed rate-hike wagers, the policy-sensitive two-year experienced more than a 20 basis point swing for the seventh straight session. analysts predict that the next increase will be at 25bps to 4.25%. recognizing phenomena like tech stock declines during rising interest rates reflect sector-specific capital cost sensitivities requires contextual expertise beyond pattern recognition (figure 1 ). these challenges expose limitations in current approaches. end-to-end methods flatten data inputs without hierarchical processing and lack domainspecific contextual interpretation @xcite @xcite . large language models (llms), despite reasoning capabilities and parametric knowledge @xcite @xcite , fail to systematically extract multi-level insights or consistently augment narratives with relevant domain expertise @xcite . ## related work data narration has evolved from template-based approaches @xcite to neural encoder-decoder architectures @xcite that improved fluency but lacked analytical capabilities. while recent llms advance narration through improved coherence and reasoning @xcite @xcite , they face two limitations: (1) processing data as flattened inputs, lacking the multi-level methodical analysis needed for comprehensive insight extraction @xcite ; and (2) inconsistently applying inherent domain knowledge, producing narratives that may miss contextual interpretations @xcite . these limitations underscore the need for structured frameworks that explicitly guide llms in both hierarchical analysis and domain knowledge application. ## kahan framework structure kahan extracts insights from raw tabular data through domain knowledge-guided hierarchical analysis-from single entities, to pairs and groups, to system-level-to output coherent narratives. we detail this three-stage process (figure 2 ) with reference to the running example in figure 3 , which generates the report in figure 1 . ## evaluation we evaluate kahan against baseline approaches using various models and evaluation settings. ## results and analyses ## discussion: scaling factors and cross-domain applicability we examine three key factors that influence ka-han's effectiveness: (1) knowledge quality, comparing performance when domain knowledge is generated using different models and methods, (2) market complexity, assessing how our hierarchical approach performs on markets with varying numbers of entities (few vs. many), and (3) domain transferability, evaluating how our framework generalizes beyond financial data to healthcare analytics. ## conclusion this paper introduces kahan, a knowledgeaugmented hierarchical framework that transforms financial data into coherent narratives. our approach outperforms baseline methods while revealing insights about effective narrative systems. knowledge augmentation enhances narrative quality while maintaining factuality, enables distillation to smaller models, and transfers effectively to other expertise-intensive domains. hierarchical analysis improves insight discovery and narrative organization, with benefits varying by market complexity and practical utility validated by financial experts. future research directions include: extending knowledge augmentation by incorporating realtime market news @xcite , developing multi-modal frameworks with visualizations to balance readability and analysis depth (chy and buadi, 2023), integrating predictive analytics for actionable insights based on identified causal relationships @xcite .