mlsanityData to 14 Oct 2026

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

arXiv 2404.19063 ↗First posted 29 Apr 2024cs.CLcited by 11

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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.

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.

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1 of 5 references.

References · 1

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 3.

11 papers.

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  1. 2023FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models
  2. 2024SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models
  3. 2025BizFinBench: A Business-Driven Real-World Financial Benchmark for Evaluating LLMs
  4. 2026FinRiskAtlas: Decision-Aligned Evaluation of Large Language Models for Financial Risk Review
  5. 2025FinEval-KR: A Financial Domain Evaluation Framework for Large Language Models' Knowledge and Reasoning
  6. 2026Evaluating Financial Intelligence in Large Language Models: Benchmarking SuperInvesting AI with LLM Engines
  7. 2025Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning
  8. 2024FinBen: A Holistic Financial Benchmark for Large Language Models