Summary of Labsafety Bench: Benchmarking Llms on Safety Issues in Scientific Labs, by Yujun Zhou et al.
LabSafety Bench: Benchmarking LLMs on Safety Issues in Scientific Labs
by Yujun Zhou, Jingdong Yang, Yue Huang, Kehan Guo, Zoe Emory, Bikram Ghosh, Amita Bedar, Sujay Shekar, Pin-Yu Chen, Tian Gao, Werner Geyer, Nuno Moniz, Nitesh V Chawla, Xiangliang Zhang
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Laboratory Safety Benchmark (LabSafety Bench) evaluates large language models (LLMs) and vision language models (VLMs) on their ability to identify potential hazards, assess risks, and predict the consequences of unsafe actions in lab environments. The benchmark comprises 765 multiple-choice questions aligned with US Occupational Safety and Health Administration (OSHA) protocols, along with 520 realistic laboratory scenarios featuring dual evaluation tasks: Hazards Identification Test and Consequence Identification Test. Evaluations across eight proprietary models, seven open-weight LLMs, and four VLMs reveal that no model achieves the safety threshold required for reliable operation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial Intelligence (AI) is changing how scientists work, but this can be dangerous if AI isn’t used safely. Researchers might think their AI tools are too good to fail, but that’s not always true. In fact, most AI models don’t do well when they’re asked to identify hazards and predict the consequences of bad actions in labs. The LabSafety Bench is a test to see how well AI does on these tasks. It has 765 multiple-choice questions and 520 scenarios where AI has to show what it knows. |
