The current study evaluated the accuracy of five pre-trained large language models (LLMs) in matching human judgment for standard-to-standard alignment study. Results demonstrated comparable performance LLMs across despite differences in scale and computational demands. Additionally, incorporating domain labels as auxiliary information did not enhance LLMs performance. These findings provide initial evidence for the viability of open-source LLMs to facilitate alignment study and offer insights into the utility of auxiliary information.