Pith. sign in

REVIEW 2 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2404.19063 v1 pith:3JXQ5SNK submitted 2024-04-29 cs.CL

classification cs.CL
keywords financialchinesebenchmarkflmsmodelssc-fincomplianceknowledge
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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}}.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CFBenchmark-MM: Chinese Financial Assistant Benchmark for Multimodal Large Language Model

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A 9,356-pair Chinese multimodal financial benchmark reveals that state-of-the-art multimodal LLMs, including GPT-4V, still score below 53% on objective and 39% on subjective financial chart tasks.

  2. FinLMM-R1: Enhancing Financial Reasoning in LMM through Scalable Data and Reward Design

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A two-stage RL framework with length, image-selection, and adversarial rewards, trained on 89,378 ASP-built financial image-question pairs, improves multimodal reasoning over LMM-R1.

Pith tools