Tables | Web IR NLP Group @ NUS

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

Wed, 05 Nov 2025 00:00:00 +0000

Analytical Report Generation with LLMs

Tue, 12 Nov 2024 00:00:00 +0000

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.

SCITAB – A Benchmark for Scientific Table-Based Fact-Checking

Wed, 06 Dec 2023 00:00:00 +0000

SCITAB was developed to fill the gap in scientific fact-checking benchmarks by incorporating claims derived from actual scientific papers rather than crowd-sourced ones. These claims require multi-step reasoning to verify against evidence in tables. The dataset was created using a human–model collaboration approach, where real-world claims were filtered, counterclaims were generated using InstructGPT, and all claims were manually verified by expert annotators.

The dataset has been extensively evaluated with state-of-the-art models, including TAPAS, TAPEX, Vicuna, and GPT-4, revealing that existing table-based models struggle with SCITAB. Only GPT-4 shows notable improvements, highlighting the dataset’s challenge and potential for advancing research in table-based reasoning.

SCITAB was presented at EMNLP 2023, and the dataset is publicly available for further research. Researchers interested in compositional reasoning, table fact-checking, and model robustness are encouraged to explore SCITAB and contribute to its future developments.