SuperCLUE-Fin: Graded Fine-Grained Analysis of Chinese LLMs on Diverse Financial Tasks and Applications
- URL: http://arxiv.org/abs/2404.19063v1
- Date: Mon, 29 Apr 2024 19:04:35 GMT
- Title: SuperCLUE-Fin: Graded Fine-Grained Analysis of Chinese LLMs on Diverse Financial Tasks and Applications
- Authors: Liang Xu, Lei Zhu, Yaotong Wu, Hang Xue,
- Abstract summary: SC-Fin 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.
Using multi-turn, open-ended conversations that mimic real-life scenarios, SC-Fin measures models on a range of criteria.
- Score: 17.34850312139675
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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..~\footnote{Our benchmark can be found at \url{https://www.CLUEbenchmarks.com}}.
Related papers
- CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models [61.324062412648075]
CFinBench is an evaluation benchmark for assessing the financial knowledge of large language models (LLMs) under Chinese context.
It comprises 99,100 questions spanning 43 second-level categories with 3 question types: single-choice, multiple-choice and judgment.
The results show that GPT4 and some Chinese-oriented models lead the benchmark, with the highest average accuracy being 60.16%.
arXiv Detail & Related papers (2024-07-02T14:34:36Z) - Financial Knowledge Large Language Model [4.599537455808687]
We introduce IDEA-FinBench, an evaluation benchmark for assessing financial knowledge in large language models (LLMs)
We propose IDEA-FinKER, a framework designed to facilitate the rapid adaptation of general LLMs to the financial domain.
Finally, we present IDEA-FinQA, a financial question-answering system powered by LLMs.
arXiv Detail & Related papers (2024-06-29T08:26:49Z) - FinBen: A Holistic Financial Benchmark for Large Language Models [75.09474986283394]
FinBen is the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks.
FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading.
arXiv Detail & Related papers (2024-02-20T02:16:16Z) - Revolutionizing Finance with LLMs: An Overview of Applications and
Insights [47.11391223936608]
Large Language Models (LLMs) like ChatGPT have seen considerable advancements and have been applied in diverse fields.
These models are being utilized for automating financial report generation, forecasting market trends, analyzing investor sentiment, and offering personalized financial advice.
arXiv Detail & Related papers (2024-01-22T01:06:17Z) - CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model [22.127509074325324]
Large language models (LLMs) have demonstrated great potential in the financial domain.
In this work, we introduce CFBenchmark, to evaluate the performance of LLMs for Chinese financial assistant.
arXiv Detail & Related papers (2023-11-10T01:12:03Z) - Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4
on mock CFA Exams [26.318005637849915]
This study aims at assessing the financial reasoning capabilities of Large Language Models (LLMs)
We leverage mock exam questions of the Chartered Financial Analyst (CFA) Program to conduct a comprehensive evaluation of ChatGPT and GPT-4.
We present an in-depth analysis of the models' performance and limitations, and estimate whether they would have a chance at passing the CFA exams.
arXiv Detail & Related papers (2023-10-12T19:28:57Z) - Empowering Many, Biasing a Few: Generalist Credit Scoring through Large
Language Models [53.620827459684094]
Large Language Models (LLMs) have great potential for credit scoring tasks, with strong generalization ability across multiple tasks.
We propose the first open-source comprehensive framework for exploring LLMs for credit scoring.
We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks.
arXiv Detail & Related papers (2023-10-01T03:50:34Z) - CMMLU: Measuring massive multitask language understanding in Chinese [133.70911295934746]
This paper introduces a comprehensive Chinese benchmark that covers various subjects, including natural science, social sciences, engineering, and humanities.
CMMLU fills the gap in evaluating the knowledge and reasoning capabilities of large language models within the Chinese context.
arXiv Detail & Related papers (2023-06-15T15:49:51Z) - PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark
for Finance [63.51545277822702]
PIXIU is a comprehensive framework including the first financial large language model (LLMs) based on fine-tuning LLaMA with instruction data.
We propose FinMA by fine-tuning LLaMA with the constructed dataset to be able to follow instructions for various financial tasks.
We conduct a detailed analysis of FinMA and several existing LLMs, uncovering their strengths and weaknesses in handling critical financial tasks.
arXiv Detail & Related papers (2023-06-08T14:20:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.