While automated labeling processes offer efficiency and scalability benefits in categorizing questions for SALAD-Bench, there are potential limitations and biases that could arise from this approach. One limitation is related to the quality of automated labels generated by Language Models (LLMs). Despite their advanced capabilities in natural language processing tasks, LLMs may still exhibit biases or inaccuracies when assigning categories to questions. This could result in misclassification errors or incorrect labeling that might impact the overall integrity of the dataset.
Another potential limitation is the lack of nuanced understanding or contextual awareness exhibited by automated labeling systems. Human annotators often possess domain-specific knowledge or critical thinking skills that enable them to make informed decisions about question categorization based on subtle nuances or context clues present in the data. Automated systems may struggle with such nuanced distinctions, leading to oversimplification or misinterpretation during label assignment.
Moreover, biases inherent in training data used for fine-tuning automated labeling models can propagate into categorization outcomes. If training datasets contain skewed representations or discriminatory patterns related to certain categories within SALAD-Bench's taxonomy, it could introduce bias into how questions are labeled automatically.
To address these limitations effectively while leveraging automation benefits,
it is essential for researchers using automated labeling processes
to implement rigorous validation checks,
incorporate human oversight where necessary,
and continuously monitor model performance
to ensure accurate categorization results without introducing unintended biases.