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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 seemingly toxic 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 rejection rate on seemingly toxic prompts 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 seemingly toxic prompts. | |
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| <!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #555555; font-weight: bold">@article</span>{cui2024or, | |
| title<span style="color: #333333">=</span>{OR<span style="color: #333333">-</span>Bench: An Over<span style="color: #333333">-</span>Refusal Benchmark <span style="color: #008800; font-weight: bold">for</span> Large Language Models}, | |
| author<span style="color: #333333">=</span>{Cui, Justin <span style="color: #000000; font-weight: bold">and</span> Chiang, Wei<span style="color: #333333">-</span>Lin <span style="color: #000000; font-weight: bold">and</span> Stoica, Ion <span style="color: #000000; font-weight: bold">and</span> Hsieh, Cho<span style="color: #333333">-</span>Jui}, | |
| journal<span style="color: #333333">=</span>{arXiv preprint arXiv:<span style="color: #6600EE; font-weight: bold">2405.20947</span>}, | |
| year<span style="color: #333333">=</span>{<span style="color: #0000DD; font-weight: bold">2024</span>} | |
| } | |
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