# CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model

arXiv: https://arxiv.org/abs/2311.05812 · HTML: https://mlsanity.com/paper/2311.05812/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Fast Transformer Decoding: One Write-Head is All You Need](https://mlsanity.com/paper/1911.02150/) (2019)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2107.02137/) (2021)
- [PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance](https://mlsanity.com/paper/2306.05443/) (2023)
- [Evaluating the Performance of Large Language Models on GAOKAO Benchmark](https://mlsanity.com/paper/2305.12474/) (2023)
- [PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation](https://mlsanity.com/paper/2104.12369/) (2021)
- [BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark](https://mlsanity.com/paper/2302.09432/) (2023)

## Built on by (most cited first, 18 of 18)

- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation](https://mlsanity.com/paper/2505.24714/) (2025)
- [Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarking](https://mlsanity.com/paper/2505.11065/) (2025)
- [CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models](https://mlsanity.com/paper/2504.13534/) (2025)
- [FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol](https://mlsanity.com/paper/2603.24943/) (2026)
- [MME-Finance: A Multimodal Finance Benchmark for Expert-level Understanding and Reasoning](https://mlsanity.com/paper/2411.03314/) (2024)
- [An Agent Framework for Real-Time Financial Information Searching with Large Language Models](https://mlsanity.com/paper/2502.15684/) (2024)
- [Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models](https://mlsanity.com/paper/2411.06272/) (2024)
- [Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges](https://mlsanity.com/paper/2507.18577/) (2025)
- [Financial Knowledge Large Language Model](https://mlsanity.com/paper/2407.00365/) (2024)
- [Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learning](https://mlsanity.com/paper/2505.18831/) (2025)
- [CPSDBench: A Large Language Model Evaluation Benchmark and Baseline for Chinese Public Security Domain](https://mlsanity.com/paper/2402.07234/) (2024)
- [FinSphere, a Real-Time Stock Analysis Agent Powered by Instruction-Tuned LLMs and Domain Tools](https://mlsanity.com/paper/2501.12399/) (2025)
- [FinEval-KR: A Financial Domain Evaluation Framework for Large Language Models' Knowledge and Reasoning](https://mlsanity.com/paper/2506.21591/) (2025)
- [FinReasoning: A Hierarchical Benchmark for Reliable Financial Research Reporting](https://mlsanity.com/paper/2603.19254/) (2026)
- [FinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming](https://mlsanity.com/paper/2606.19887/) (2026)
- [FinFIRST: Benchmarking Search Agents for Financial Information Retrieval, Sourcing and Traceability](https://mlsanity.com/paper/2609.25192/) (2026)
- [LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs](https://mlsanity.com/paper/2609.33470/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have demonstrated great potential in the financial domain. Thus, it becomes important to assess the performance of LLMs in the financial tasks. In this work, we introduce CFBenchmark, to evaluate the performance of LLMs for Chinese financial assistant. The basic version of CFBenchmark is designed to evaluate the basic ability in Chinese financial text processing from three aspects~(i.e. recognition, classification, and generation) including eight tasks, and includes financial texts ranging in length from 50 to over 1,800 characters. We conduct experiments on several LLMs available in the literature with CFBenchmark-Basic, and the experimental results indicate that while some LLMs show outstanding performance in specific tasks, overall, there is still significant room for improvement in basic tasks of financial text processing with existing models. In the future, we plan to explore the advanced version of CFBenchmark, aiming to further explore the extensive capabilities of language models in more profound dimensions as a financial assistant in Chinese. Our codes are released at https://github.com/TongjiFinLab/CFBenchmark.