Analytical Report Generation with LLMs

While LLMs excel in language understanding and can pass professional financial examinations, they struggle to generate expert-level analytical reasoning from real data. In this project, we explore analytical report generation using LLMs, turning complex market data and corporate disclosures into actionable, decision-relevant narratives. We first develop DataTales (EMNLP 2024), a benchmark pairing 4.9k financial market data tables with human reports to diagnose the exact gap between LLM generation and human creation. Secondly, we propose KAHAN (Findings of EMNLP 2025), a knowledge-augmented hierarchical analysis framework that coordinates analysis from entity metrics to market-wide patterns using executable code. Thirdly, we introduce AnalysisBank (EMNLP 2026), a distilled library of over 5,300 expert analysis patterns that conditions reasoning directly on observed data signals to generate novel, data-specific insights.

Yajing, a PhD student at WING and a Senior Data Scientist at Rio Tinto, started her research driven by the real-world business need for timely daily market reports. While working alongside market professionals, she observed how human commentary takes hours to draft while existing automation defaults to shallow summaries. Advised by Prof. Min-Yen Kan, she formalized this industry pain point into a systematic study of execution failures in generative models. Together with their collaborators, they developed structured methodologies to decouple calculation from composition and condition reasoning moves directly on observed data patterns. Their work demonstrates that grounded analytical depth can be systematically achieved across financial modalities and transferred to domains like medical diagnostics and scientific writing.