Taxonomy-based CheckList for Large Language Model Evaluation
Damin Zhang
As large language models (LLMs) have been used in many downstream tasks, the
internal stereotypical representation may affect the fairness of the outputs.
In this work, we introduce human knowledge into natural language interventions
and study pre-trained language models' (LMs) behaviors within the context of
gender bias. Inspired by CheckList behavioral testing, we present a
checklist-style task that aims to probe and quantify LMs' unethical behaviors
through question-answering (QA). We design three comparison studies to evaluate
LMs from four aspects: consistency, biased tendency, model preference, and
gender preference switch. We probe one transformer-based QA model trained on
SQuAD-v2 dataset and one autoregressive large language model. Our results
indicate that transformer-based QA model's biased tendency positively
correlates with its consistency, whereas LLM shows the opposite relation. Our
proposed task provides the first dataset that involves human knowledge for LLM
bias evaluation.