# FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models

arXiv: https://arxiv.org/abs/2401.02982 · HTML: https://mlsanity.com/paper/2401.02982/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 1 Jan 2024.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://mlsanity.com/paper/2304.06364/) (2023)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering](https://mlsanity.com/paper/2210.03849/) (2022)
- [XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters](https://mlsanity.com/paper/2305.12002/) (2023)
- [BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark](https://mlsanity.com/paper/2302.09432/) (2023)
- [nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task](https://mlsanity.com/paper/2112.12926/) (2021)

## Built on by (most cited first, 11 of 11)

- [MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering](https://mlsanity.com/paper/2503.16858/) (2025)
- [Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance -- A Case Study in Finance](https://mlsanity.com/paper/2410.01109/) (2024)
- [Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows](https://mlsanity.com/paper/2512.13168/) (2025)
- [PortBench: A Correlation-Aware, Full-Pipeline Benchmark for LLM-Driven Portfolio Management](https://mlsanity.com/paper/2605.27887/) (2026)
- [FinBalance: A Multi-Document Accounting Reconciliation Benchmark](https://mlsanity.com/paper/2606.15949/) (2026)
- [The Illusionist's Prompt: Exposing the Factual Vulnerabilities of Large Language Models with Linguistic Nuances](https://mlsanity.com/paper/2504.02865/) (2025)
- [JT-DA: Enhancing Data Analysis with Tool-Integrated Table Reasoning Large Language Models](https://mlsanity.com/paper/2512.06859/) (2025)
- [An Information-Theoretic Framework for Robust Large Language Model Editing](https://mlsanity.com/paper/2512.16227/) (2025)
- [AWARE-FX: An Auditable Knowledge-Guided AI System for Measuring Corporate Foreign-Exchange Hedging Disclosure](https://mlsanity.com/paper/2607.27611/) (2026)
- [Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents](https://mlsanity.com/paper/2608.06108/) (2026)
- [FinFIRST: Benchmarking Search Agents for Financial Information Retrieval, Sourcing and Traceability](https://mlsanity.com/paper/2609.25192/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks. However, their proficiency and reliability in the specialized domain of financial data analysis, particularly focusing on data-driven thinking, remain uncertain. To bridge this gap, we introduce textttFinDABench, a comprehensive benchmark designed to evaluate the financial data analysis capabilities of LLMs within this context. textttFinDABench assesses LLMs across three dimensions: 1) Foundational Ability, evaluating the models' ability to perform financial numerical calculation and corporate sentiment risk assessment; 2) Reasoning Ability, determining the models' ability to quickly comprehend textual information and analyze abnormal financial reports; and 3) Technical Skill, examining the models' use of technical knowledge to address real-world data analysis challenges involving analysis generation and charts visualization from multiple perspectives. We will release textttFinDABench, and the evaluation scripts at urlhttps://github.com/cubenlp/BIBench. textttFinDABench aims to provide a measure for in-depth analysis of LLM abilities and foster the advancement of LLMs in the field of financial data analysis.