Assessment of chemistry knowledge in large language models that generate code
Andrew Dickson White, Glen M. Hocky, Heta A. Gandhi, Mehrad Ansari, Sam Cox, Geemi P. Wellawatte, Subarna Sasmal, Ziyue Yang, Kangxin Liu, Yuvraj Singh, Willmor J. Peña Ccoa
Abstract
prompt engineering strategies, like putting copyright notices at the top of files. Our dataset and evaluation tools are open source which can be contributed to or built upon by future researchers, and will serve as a community resource for evaluating the performance of new models as they emerge. We also describe some good practices for employing LLMs in chemistry. The general success of these models demonstrates that their impact on chemistry teaching and research is poised to be enormous.
Topics & Concepts
Computer scienceCode (set theory)Programming languageSet (abstract data type)Machine Learning in Materials ScienceTopic ModelingChemical Synthesis and Analysis