Summary of Benchmarking Large Language Models on Cflue — a Chinese Financial Language Understanding Evaluation Dataset, by Jie Zhu and Junhui Li and Yalong Wen and Lifan Guo
Benchmarking Large Language Models on CFLUE – A Chinese Financial Language Understanding Evaluation Dataset
by Jie Zhu, Junhui Li, Yalong Wen, Lifan Guo
First submitted to arxiv on: 17 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new benchmark, Chinese Financial Language Understanding Evaluation (CFLUE), to assess the capabilities of large language models (LLMs) in various dimensions. CFLUE includes datasets for both knowledge assessment and application assessment. Knowledge assessment consists of 38K+ multiple-choice questions with solution explanations, serving dual purposes: answer prediction and question reasoning. Application assessment features 16K+ test instances across NLP tasks such as text classification, machine translation, relation extraction, reading comprehension, and text generation. The authors conduct a thorough evaluation of representative LLMs on CFLUE and find that only GPT-4 and GPT-4-turbo achieve an accuracy exceeding 60% in answer prediction for knowledge assessment, indicating room for improvement. In application assessment, GPT-4 and GPT-4-turbo are top performers, but their advantage over lightweight LLMs is diminished. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new test to see how well large language models can understand Chinese financial language. The test has two parts: one that checks if the model knows the answers to questions, and another that sees how well it does at tasks like translating text or finding relationships between ideas. The researchers tested some popular language models on this test and found that only two of them were really good at understanding the language – but even they didn’t do perfectly. This means there’s still work to be done to make these language models better. |
Keywords
» Artificial intelligence » Gpt » Language understanding » Nlp » Text classification » Text generation » Translation
