Categorizing challenging queries for LLM evaluation introduces potential drawbacks and biases that need careful consideration. One drawback is the subjective nature of categorization criteria, which may vary depending on individual annotators' interpretations or preconceptions about what constitutes a challenging query type. This subjectivity could lead to inconsistencies or inaccuracies in labeling queries across different evaluators.
Biases may also arise from predefined categories used for classification within benchmarks like HaluEval-Wild. The predetermined categories might not encompass all possible variations or nuances present in user queries that could induce hallucinations in LLMs. As a result, certain types of challenging inputs may be overlooked or misclassified due to limitations inherent in the categorization framework.
Furthermore, there is a risk of introducing confirmation bias during manual verification processes aimed at validating categorized queries. Human verifiers may inadvertently reinforce existing biases by selectively confirming instances that align with their expectations while overlooking contradictory examples that challenge established norms.
To mitigate these drawbacks and biases, it is essential to establish clear guidelines for categorizing challenging queries based on objective criteria whenever possible. Implementing inter-rater reliability checks and regular calibration sessions among annotators can help ensure consistency and reduce subjectivity in query classification.