Probing Association Biases in LLM Moderation Over-Sensitivity
This addresses over-sensitivity in LLM moderation for users and platforms, providing insights into underlying mechanisms, but it is incremental as it builds on prior work on biases.
The paper tackled the problem of LLMs misclassifying benign comments as toxic in content moderation by revealing systematic topic biases beyond offensive terms, finding that advanced models like GPT-4 Turbo show stronger topic stereotypes despite lower false positive rates.
Large Language Models are widely used for content moderation but often misclassify benign comments as toxic, leading to over-sensitivity. While previous research attributes this issue primarily to the presence of offensive terms, we reveal a potential cause beyond token level: LLMs exhibit systematic topic biases in their implicit associations. Inspired by cognitive psychology's implicit association tests, we introduce Topic Association Analysis, a semantic-level approach to quantify how LLMs associate certain topics with toxicity. By prompting LLMs to generate free-form scenario imagination for misclassified benign comments and analyzing their topic amplification levels, we find that more advanced models (e.g., GPT-4 Turbo) demonstrate stronger topic stereotype despite lower overall false positive rates. These biases suggest that LLMs do not merely react to explicit, offensive language but rely on learned topic associations, shaping their moderation decisions. Our findings highlight the need for refinement beyond keyword-based filtering, providing insights into the underlying mechanisms driving LLM over-sensitivity.