Summary of Enviroexam: Benchmarking Environmental Science Knowledge Of Large Language Models, by Yu Huang et al.
EnviroExam: Benchmarking Environmental Science Knowledge of Large Language Models
by Yu Huang, Liang Guo, Wanqian Guo, Zhe Tao, Yang Lv, Zhihao Sun, Dongfang Zhao
First submitted to arxiv on: 18 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 A comprehensive evaluation method called EnviroExam is proposed to assess the knowledge of large language models in the field of environmental science. This method is based on curricula from top international universities, covering undergraduate, master’s, and doctoral courses, and includes 936 questions across 42 core courses. The performance differences among 31 open-source large language models are evaluated through 0-shot and 5-shot tests, revealing that 61.3% of the models passed the 5-shot tests, while 48.39% passed the 0-shot tests. A coefficient of variation is introduced as an indicator to evaluate the performance of mainstream open-source large language models in environmental science from multiple perspectives, providing effective criteria for selecting and fine-tuning language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EnviroExam is a new way to test big computer programs that can understand human language. These programs are important for helping us with tasks like reducing waste and protecting the environment. The program tests 31 different types of these language models using questions from real university courses. It shows which ones do well on their own (without any help) and which ones need some extra training to get good results. This helps us choose the best programs for our environmental work. |
Keywords
» Artificial intelligence » Fine tuning
