Summary of Scisafeeval: a Comprehensive Benchmark For Safety Alignment Of Large Language Models in Scientific Tasks, by Tianhao Li et al.
SciSafeEval: A Comprehensive Benchmark for Safety Alignment of Large Language Models in Scientific Tasks
by Tianhao Li, Jingyu Lu, Chuangxin Chu, Tianyu Zeng, Yujia Zheng, Mei Li, Haotian Huang, Bin Wu, Zuoxian Liu, Kai Ma, Xuejing Yuan, Xingkai Wang, Keyan Ding, Huajun Chen, Qiang Zhang
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 Large language models (LLMs) are revolutionizing various scientific tasks across biology, chemistry, medicine, and physics. However, ensuring their safety alignment in research remains understudied. Current benchmarks primarily focus on textual content, neglecting crucial scientific representations like molecular, protein, and genomic languages. Additionally, the safety mechanisms of LLMs in scientific tasks are insufficiently explored. To address these limitations, we introduce SciSafeEval, a comprehensive benchmark evaluating the safety alignment of LLMs across various scientific tasks, spanning multiple languages (textual, molecular, protein, and genomic) and domains. We evaluate LLMs in zero-shot, few-shot, and chain-of-thought settings, and introduce a “jailbreak” feature that challenges safety-equipped LLMs to rigorously test their defenses against malicious intentions. Our benchmark outperforms existing safety datasets in scale and scope, providing a robust platform for assessing the performance and safety of LLMs in scientific contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are making big changes in science by helping with tasks like biology, chemistry, medicine, and physics. But there’s an important problem: making sure these models don’t get used for bad things. Right now, most benchmarks only test how well they do on words, not the special languages scientists use to describe molecules, proteins, and genes. We created a new benchmark called SciSafeEval that tests LLMs in many different areas of science using various languages and domains. We also tested LLMs with safety features to see if they can withstand attempts to trick them into doing bad things. |
Keywords
» Artificial intelligence » Alignment » Few shot » Zero shot
