Reject Rate (RR)

The Reject Rate is a metric used to evaluate the frequency at which a large language model (LLM) refuses to provide a response to a query. It is particularly relevant in scenarios where refusal is expected to mitigate risks associated with unsafe, biased, or non-ethical outputs. This metric helps measure the robustness and safety of an LLM by analyzing its ability to appropriately reject or decline unsafe requests.
 

Applicable Models:
Reject Rate is commonly applied to large language models, particularly those designed for conversational AI or content generation tasks.
 

Background:
With the increasing deployment of LLMs across diverse sectors, ensuring they do not generate harmful or inappropriate content has become a priority. Reject Rate evaluates refusal mechanisms in response to potentially unsafe, adversarial, or ambiguous inputs. Research such as SORRY-Bench and OR-Bench (e.g., Xie et al., 2024; Cui et al., 2024) underscores the importance of balancing refusal behavior with performance.
 

Formulae:
The Reject Rate is computed as:

Reject Rate = (Number of Rejected Queries) / (Total Number of Queries)

Where:

• Rejected Queries: Instances where the model explicitly refuses to provide an output.
• Total Queries: All inputs provided to the model during evaluation.
   

Applications:

• Robustness Assessment: Evaluating how well a model handles adversarial or ambiguous inputs.
• Safety Checks: Ensuring the model avoids generating harmful or biased content.
• System Tuning: Fine-tuning refusal mechanisms in AI systems for compliance with ethical and regulatory guidelines.
   

Impact:
The Reject Rate is critical for maintaining the safety and reliability of AI systems. A well-calibrated reject rate ensures models refuse unsafe queries while minimizing the over-refusal of benign ones, which could degrade usability and user experience. It also provides transparency to developers and users about the system's safety mechanisms.

• Xie, T., Qi, X., Zeng, Y., Huang, Y., Sehwag, U. M., Huang, K., He, L., Wei, B., Li, D., Sheng, Y., Jia, R., Li, B., Li, K., Chen, D., Henderson, P., & Mittal, P. (2024). SORRY-Bench: Systematically evaluating large language model safety refusal behaviors. arXiv preprint arXiv:2406.14598. https://arxiv.org/abs/2406.14598
   
• Cui, J., Chiang, W.-L., Stoica, I., & Hsieh, C.-J. (2024). OR-Bench: An over-refusal benchmark for large language models. arXiv preprint arXiv:2405.20947. https://arxiv.org/abs/2405.20947