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| <div class="title">OR-BENCH</div> | |
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| An over-refusal benchmark for large language models | |
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| <b>Large Language Models (LLMs) </b> require careful safety alignment to prevent malicious outputs. While significant research focuses on mitigating harmful content generation, | |
| the enhanced safety often come with the side effect of over-refusal, where LLMs may reject innocuous prompts and become less helpful. | |
| Although the issue of over-refusal has been empirically observed, a systematic measurement is challenging | |
| due to the difficulty of crafting prompts that appear harmful but are benign.<br><br> | |
| We introduce OR-Bench, the <b>first large-scale over-refusal benchmark</b>. OR-Bench comprises 80,000 over-refusal prompts across 10 common rejection categories, a subset of around 1,000 hard prompts that are challenging even for state-of-the-art LLMs, and an additional 600 toxic prompts to prevent indiscriminate responses.<br><br> | |
| We plot the evaluation results in the following figure. The x-axis is the over-refusal rate and the y-axis is the rejection rate on real toxic prompts. In the ideal case, the model should be on the top-left corner where the model rejects the most number of toixc prompts and the least number of safe prompts. | |
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