This paper investigates gender bias in Large Language Model (LLM)-generated teacher evaluations in higher education setting, focusing on evaluations produced by GPT-4 across six academic subjects. By applying a comprehensive analytical framework that includes Odds Ratio (OR) analysis, Word Embedding Association Test (WEAT), sentiment analysis, and contextual analysis, this paper identified patterns of gender-associated language reflecting societal stereotypes. Specifically, words related to approachability and support were used more frequently for female instructors, while words related to entertainment were predominantly used for male instructors, aligning with the concepts of communal and agentic behaviors. The study also found moderate to strong associations between male salient adjectives and male names, though career and family words did not distinctly capture gender biases. These findings align with prior research on societal norms and stereotypes, reinforcing the notion that LLM-generated text reflects existing biases.

Pre-trained large language models (LLMs) can now be easily adapted for specific business purposes using custom prompts or fine tuning. These customizations are often iteratively re-engineered to improve some aspect of performance, but after each change businesses want to ensure that there has been no negative impact on the system's behavior around such critical issues as bias. Prior methods of benchmarking bias use techniques such as word masking and multiple choice questions to assess bias at scale, but these do not capture all of the nuanced types of bias that can occur in free response answers, the types of answers typically generated by LLM systems. In this paper, we identify several kinds of nuanced bias in free text that cannot be similarly identified by multiple choice tests. We describe these as: confidence bias, implied bias, inclusion bias and erasure bias. We present a semi-automated pipeline for detecting these types of bias by first eliminating answers that can be automatically classified as unbiased and then co-evaluating name reversed pairs using crowd workers. We believe that the nuanced classifications our method generates can be used to give better feedback to LLMs, especially as LLM reasoning capabilities become more advanced.