# SuperCLUE-Fin: Graded Fine-Grained Analysis of Chinese LLMs on Diverse Financial Tasks and Applications

arXiv: https://arxiv.org/abs/2404.19063 · HTML: https://mlsanity.com/paper/2404.19063/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)

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

- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [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)
- [SoK: Security of Autonomous LLM Agents in Agentic Commerce](https://mlsanity.com/paper/2604.15367/) (2026)
- [SECQUE: A Benchmark for Evaluating Real-World Financial Analysis Capabilities](https://mlsanity.com/paper/2504.04596/) (2025)
- [Enterprise Benchmarks for Large Language Model Evaluation](https://mlsanity.com/paper/2410.12857/) (2024)
- [CFBenchmark-MM: Chinese Financial Assistant Benchmark for Multimodal Large Language Model](https://mlsanity.com/paper/2506.13055/) (2025)
- [FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios](https://mlsanity.com/paper/2605.00706/) (2026)
- [Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation](https://mlsanity.com/paper/2502.17011/) (2025)
- [FinLMM-R1: Enhancing Financial Reasoning in LMM through Scalable Data and Reward Design](https://mlsanity.com/paper/2506.13066/) (2025)
- [CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases](https://mlsanity.com/paper/2608.27391/) (2026)
- [Towards Competent AI for Fundamental Analysis in Finance: A Benchmark Dataset and Evaluation](https://mlsanity.com/paper/2506.07315/) (2025)

## Abstract (arXiv)

The SuperCLUE-Fin (SC-Fin) benchmark is a pioneering evaluation framework tailored for Chinese-native financial large language models (FLMs). It assesses FLMs across six financial application domains and twenty-five specialized tasks, encompassing theoretical knowledge and practical applications such as compliance, risk management, and investment analysis. Using multi-turn, open-ended conversations that mimic real-life scenarios, SC-Fin measures models on a range of criteria, including accurate financial understanding, logical reasoning, clarity, computational efficiency, business acumen, risk perception, and compliance with Chinese regulations. In a rigorous evaluation involving over a thousand questions, SC-Fin identifies a performance hierarchy where domestic models like GLM-4 and MoonShot-v1-128k outperform others with an A-grade, highlighting the potential for further development in transforming theoretical knowledge into pragmatic financial solutions. This benchmark serves as a critical tool for refining FLMs in the Chinese context, directing improvements in financial knowledge databases, standardizing financial interpretations, and promoting models that prioritize compliance, risk management, and secure practices. We create a contextually relevant and comprehensive benchmark that drives the development of AI in the Chinese financial sector. SC-Fin facilitates the advancement and responsible deployment of FLMs, offering valuable insights for enhancing model performance and usability for both individual and institutional users in the Chinese market..~footnoteOur benchmark can be found at urlhttps://www.CLUEbenchmarks.com.