Summary of Chemeval: a Comprehensive Multi-level Chemical Evaluation For Large Language Models, by Yuqing Huang et al.
ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models
by Yuqing Huang, Rongyang Zhang, Xuesong He, Xuyang Zhi, Hao Wang, Xin Li, Feiyang Xu, Deguang Liu, Huadong Liang, Yi Li, Jian Cui, Zimu Liu, Shijin Wang, Guoping Hu, Guiquan Liu, Qi Liu, Defu Lian, Enhong Chen
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Chemical Physics (physics.chem-ph); Biomolecules (q-bio.BM)
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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 novel benchmark, ChemEval, is proposed to assess the capabilities of Large Language Models (LLMs) in various chemical domains. The existing benchmarks in this domain are inadequate for assessing LLMs’ performance across a range of tasks varying in type and complexity. ChemEval identifies four crucial levels in chemistry, evaluating 12 dimensions across 42 distinct chemical tasks informed by open-source data and expert-crafted data. The benchmark is designed to assess LLMs’ capabilities under zero-shot and few-shot learning contexts, using carefully selected demonstration examples and prompts. The results show that general LLMs excel in literature understanding and instruction following but struggle with advanced chemical knowledge tasks, while specialized LLMs demonstrate enhanced chemical competencies at the cost of reduced literary comprehension. This suggests significant potential for enhancing LLMs’ capabilities when tackling sophisticated tasks in chemistry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are being tested to see how well they can understand and work with chemical information. Right now, there aren’t good ways to measure how well LLMs do this. To help fix that problem, scientists created a new test called ChemEval. This test looks at 12 different ways that LLMs might be used in chemistry, like understanding what’s written in a book or following instructions. It also tests LLMs on 42 specific chemical tasks that are important for chemists to do their jobs. Some LLMs are really good at understanding what people write and following instructions, but they struggle with more complicated chemical problems. Other LLMs are better at the chemical problems, but not as good at understanding written information. |
Keywords
» Artificial intelligence » Few shot » Zero shot
