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<h1 class="title is-1 publication-title">Safety-J: Evaluating Safety with Critique</h1>
<div class="is-size-5 publication-authors">
<span class="author-block"><a href="https://github.com/yxliu0903">Yixiu Liu*</a><sup>1, 3, 4</sup>,</span>
<span class="author-block"><a href="https://github.com/zeetc">Yuxiang Zheng*</a><sup>1, 3, 4</sup>,</span>
<span class="author-block"><a href="https://shijie-xia.github.io/">Shijie Xia</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="https://github.com/StarWalkin">Yuan Guo</a><sup>1</sup>,</span>
<span class="author-block"><a href="http://jiajun.org.cn/">Jiajun Li</a><sup>4</sup>,</span>
<span class="author-block"><a href="https://dblp.org/pid/186/6867.html">Yi Tu</a><sup>4</sup>,</span>
<span class="author-block"><a href="https://www.linkedin.com/in/chaoling-s-915289131/">Chaoling Song</a><sup>4</sup>,</span>
<span class="author-block"><a href="http://pfliu.com/">Pengfei Liu</a><sup>1, 2, 3+</sup></span>
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<span class="author-block"><sup>1</sup>Shanghai Jiao Tong University,</span>
<span class="author-block"><sup>2</sup>Shanghai Artificial Intelligence Laboratory,</span>
<span class="author-block"><sup>3</sup>Generative AI Research Lab (GAIR),</span>
<span class="author-block"><sup>4</sup>Huawei Technologies Ltd.</span>
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<span class="author-block"><sup>*</sup>Co-first authors</span>
<span class="author-block"><sup>+</sup>Corresponding authors</span>
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<p>
Safety-J is an advanced bilingual (English and Chinese) safety evaluator designed to assess the safety of content generated by Large Language Models (LLMs). It provides detailed critiques and judgments, setting a new standard in AI content safety evaluation. It is featured with:
</p>
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<img src="images/method.png" width=100%/>
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<ul>
<li><b>Bilingual Capability: </b>Evaluates content in both English and Chinese.</li>
<li><b>Critique-based Judgment: </b>Offers detailed critiques alongside safety classifications.</li>
<li><b>Iterative Preference Learning: </b>Continuously improves through an innovative iterative learning process.</li>
<li><b>Comprehensive Coverage: </b>Addresses a wide range of safety scenarios, from privacy concerns to ethical issues.</li>
<li><b>Meta-evaluation Framework: </b>Includes an automated benchmark for assessing critique quality.</li>
<li><b>State-of-the-art Performance: </b>Outperforms existing open-source models and strong proprietary models like GPT-4o in safety evaluation tasks.</li>
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We release the benchmarking results on various safety-related datasets as a leaderboard.
</p>
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For the model accuracy evaluation, we test different models on five datasets: BeaverTails, DiaSafety, Jade, Flames, and WildSafety. The metric is the accuracy rate on each dataset, with an overall average (Avg) across all datasets. We report the results for various models, including large language models and specialized safety models. The "Generative" column indicates whether a model is capable of generating text (✔️) or if it's a non-generative model primarily used for classification(❌).
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<table id="table1" class="table" style="width: 71%">
<tr>
<td style="min-width: 160px;"><strong>Model</strong></td>
<td><strong>Generative</strong></td>
<td><strong>BeaverTails</strong></td>
<td><strong>DiaSafety</strong></td>
<td><strong>Jade</strong></td>
<td><strong>Flames</strong></td>
<td><strong>WildSafety</strong></td>
<td><strong>Average</strong></td>
</tr>
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<a href="https://huggingface.co/GAIR/Safety-J-v5">
<b>Safety-J (7B)</b>
</a>
</td>
<td>✔️</td>
<td>84.3</td>
<td>71.4</td>
<td>98.6</td>
<td>74.0</td>
<td>92.2</td>
<td>84.1</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/thu-coai/ShieldLM-14B-qwen">
<b>ShieldLM 14B</b>
</a>
</td>
<td>✔️</td>
<td>83.7</td>
<td>71.6</td>
<td>96.6</td>
<td>63.7</td>
<td>78.3</td>
<td>78.8</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/thu-coai/ShieldLM-7B-internlm2">
<b>ShieldLM 7B</b>
</a>
</td>
<td>✔️</td>
<td>84.0</td>
<td>67.9</td>
<td>96.4</td>
<td>62.3</td>
<td>77.9</td>
<td>77.7</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://openai.com/index/hello-gpt-4o">
<b>GPT-4o</b>
</a>
</td>
<td>✔️</td>
<td>82.3</td>
<td>56.1</td>
<td>97.8</td>
<td>71.6</td>
<td>80.3</td>
<td>77.6</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://openai.com/research/gpt-4">
<b>GPT-4</b>
</a>
</td>
<td>✔️</td>
<td>77.2</td>
<td>65.4</td>
<td>96.8</td>
<td>65.3</td>
<td>77.0</td>
<td>76.3</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://github.com/InternLM/InternLM">
<b>InternLM</b>
</a>
</td>
<td>✔️</td>
<td>80.4</td>
<td>54.0</td>
<td>92.7</td>
<td>53.3</td>
<td>78.5</td>
<td>71.8</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://platform.openai.com/docs/models/gpt-3-5">
<b>GPT-3.5</b>
</a>
</td>
<td>✔️</td>
<td>81.9</td>
<td>52.3</td>
<td>89.0</td>
<td>51.0</td>
<td>73.2</td>
<td>69.5</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://platform.openai.com/docs/guides/moderation">
<b>Moderation</b>
</a>
</td>
<td>❌</td>
<td>43.6</td>
<td>63.8</td>
<td>53.0</td>
<td>56.2</td>
<td>51.3</td>
<td>53.6</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://github.com/conversationai/perspectiveapi">
<b>Perspective</b>
</a>
</td>
<td>❌</td>
<td>46.3</td>
<td>55.8</td>
<td>48.3</td>
<td>51.7</td>
<td>57.4</td>
<td>51.9</td>
</tr>
</table>
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For the critique evaluation task, we assess models at both the critique level (Macro) and the AIU (Atomic Information Unit) level (Micro). The metrics include precision (Meta-P), recall (Meta-R), and F1 score (Meta-F1) for both levels in English and Chinese evaluations. We evaluate various models on their ability to generate accurate critiques and AIU analyses.
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<h2 class="title">BibTeX</h2>
<pre><code>@article{liu2024safety,
title={SAFETY-J: Evaluating Safety with Critique},
author={Liu, Yixiu and Zheng, Yuxiang and Xia, Shijie and Guo, Yuan and Li, Jiajun and Tu, Yi and Song, Chaoling and Liu, Pengfei},
journal={arXiv preprint arXiv:2407.17075},
year={2024}
}</code></pre>
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