SafetyBench: Evaluating the Safety of Large Language Models (2024)

Abstract: With the rapid development of Large Language Models (LLMs), increasing attention has been paid to their safety concerns. Consequently, evaluating the safety of LLMs has become an essential task for facilitating the broad applications of LLMs. Nevertheless, the absence of comprehensive safety evaluation benchmarks poses a significant impediment to effectively assess and enhance the safety of LLMs. In this work, we present SafetyBench, a comprehensive benchmark for evaluating the safety of LLMs, which comprises 11,435 diverse multiple choice questions spanning across 7 distinct categories of safety concerns. Notably, SafetyBench also incorporates both Chinese and English data, facilitating the evaluation in both languages. Our extensive tests over 25 popular Chinese and English LLMs in both zero-shot and few-shot settings reveal a substantial performance advantage for GPT-4 over its counterparts, and there is still significant room for improving the safety of current LLMs. We also demonstrate that the measured safety understanding abilities in SafetyBench are correlated with safety generation abilities. Data and evaluation guidelines are available at https://github.com/thu-coai/SafetyBench. Submission entrance and leaderboard are available at https://llmbench.ai/safety.

heading: 1 Introduction
text: Large Language Models (LLMs) have gained a growing amount of attention in recent years (Zhao et al., 2023). Since the release of ChatGPT (OpenAI, 2022), more and more LLMs are deployed to interact with humans, such as Llama (Touvron et al., 2023a,b), Claude (Anthropic, 2023) and ChatGLM (Du et al., 2022; Zeng et al., 2022). However, with the widespread development of LLMs, their safety flaws are also exposed (Weidinger et al., 2021), which could significantly hinder the safe
*Corresponding author.
and continuous development of LLMs. Various works have pointed out the safety risks of LLMs, such as privacy leakage (Zhang et al., 2023) and toxic generations (Deshpande et al., 2023).
Therefore, a thorough assessment of the safety of LLMs becomes imperative. However, comprehensive benchmarks for evaluating the safety of LLMs are scarce. In the past, certain widely used datasets have focused exclusively on specific facets of safety concerns such as toxicity (Gehman et al., 2020) and bias (Parrish et al., 2022). Notably, some recent Chinese safety assessment benchmarks (Sun et al., 2023; Xu et al., 2023) have gathered prompts spanning various categories of safety issues. However, they only provide Chinese data, and a nonnegligible challenge for these benchmarks is how to accurately evaluate the safety of responses generated by LLMs. Some recent works have begun to train advanced safety detectors (Inan et al., 2023; Zhang et al., 2024), but some unavoidable errors may still occur. Manual evaluation, while highly accurate, is a costly and time-consuming process, making it less conducive for rapid model iteration. Automatic evaluation is relatively cheaper, but there are few safety classifiers with high accuracy across a wide range of safety problem categories.
Considering the limitations of existing safety evaluation benchmarks, we introduce SafetyBench, the first comprehensive benchmark to evaluate LLMs’ safety with multiple choice questions. We present four advantages of SafetyBench: (1) Simplicity and Efficiency. In line with well-known benchmarks such as MMLU (Hendrycks et al., 2021b), SafetyBench exclusively features multiplechoice questions, each with a single correct answer, which enables automated and cost-effective evaluations of LLMs’ safety with exceptional accuracy. (2) Extensive Diversity. SafetyBench contains 11,435 diverse samples sourced from a wide range of origins, covering 7 distinct categories of safety problems, which provides a comprehensive
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assessment of the safety of LLMs. (3) Variety of Question Types. Test questions in SafetyBench encompass a diverse array of types, spanning dialogue scenarios, real-life situations, safety comparisons, safety knowledge inquiries, and many more. This diverse array ensures that LLMs are rigorously tested in various safety-related contexts and scenarios. (4) Multilingual Support. SafetyBench offers both Chinese and English data, which could facilitate the evaluation of both Chinese and English LLMs, ensuring a broader and more inclusive assessment.
With SafetyBench, we conduct experiments to evaluate the safety of 25 popular Chinese and English LLMs in both zero-shot and few-shot settings.
The summarized results are shown in Figure 2. Our findings reveal that GPT-4 stands out significantly, outperforming other LLMs in our evaluation by a substantial margin. Considering the LLMs are frequently used for generation, we also quantitatively verify that the safety understanding abilities measured by SafetyBench are correlated with safety generation abilities. In summary, the main contributions of this work are:
• We present SafetyBench, the first comprehensive bilingual benchmark that enables fast, accurate and cost-effective evaluation of LLMs’ safety with multiple choice questions.
• We conduct extensive tests over 25 popular LLMs in both zero-shot and few-shot settings,
which reveals safety flaws in current LLMs and provides guidance for improvement. We also provide evidence that the safety understanding abilities measured by SafetyBench are correlated with safety generation abilities.
• We release complete data, evaluation guidelines and a continually updated leaderboard to facilitate the assessment of LLMs’ safety.

heading: 2 Related Work

heading: 2.1 Safety Benchmarks for LLMs
text: Previous safety benchmarks mainly focus on a certain type of safety problems. The Winogender benchmark (Rudinger et al., 2018) focuses on a specific dimension of social bias: gender bias. The RealToxicityPrompts (Gehman et al., 2020) dataset contains 100K sentence-level prompts derived from English web text and paired with toxicity scores from Perspective API. This dataset is often used to evaluate language models’ toxic generations. The rise of LLMs brings up new problems to LLM evaluation (e.g., long context (Bai et al., 2023) and agent (Liu et al., 2023) abilities). So is it for safety evaluation. The BBQ benchmark (Parrish et al., 2022) can be used to evaluate LLMs’ social bias along nine social dimensions. It compares the model’s choice under both under-informative context and adequately informative context, which could reflect whether the tested models rely on stereotypes to give their answers. There are also some red-teaming studies focusing on attacking LLMs (Perez et al., 2022; Zhuo et al., 2023). Recently, two Chinese safety benchmarks (Sun et al., 2023; Xu et al., 2023) include test prompts covering various safety categories, which could make the safety evaluation for LLMs more comprehensive. Differently, SafetyBench use multiple choice questions from seven safety categories to automatically evaluate LLMs’ safety with lower cost and error.

heading: 2.2 Benchmarks Using Multiple Choice Questions
text: A number of benchmarks have deployed multiple choice questions to evaluate LLMs’ capabilities. The popular MMLU benchmark (Hendrycks et al., 2021b) consists of multi-domain and multi-task questions collected from real-word books and examinations. It is frequently used to evaluate LLMs’ world knowledge and problem solving ability. Similar Chinese benchmarks are also developed to evaluate LLMs’ world knowledge with questions from
examinations, such as C-EVAL (Huang et al., 2023) and MMCU (Zeng, 2023). AGIEval (Zhong et al., 2023) is another popular bilingual benchmark to assess LLMs in the context of human-centric standardized exams. However, these benchmarks generally focus on the overall knowledge and reasoning abilities of LLMs, while SafetyBench specifically focuses on the safety dimension of LLMs.

heading: 3 SafetyBench Construction
text: An overview of SafetyBench is presented in Figure 1. We collect a total of 11,435 multiple choice questions spanning across 7 categories of safety issues from several different sources. More examples are provided in Figure 7 in Appendix. Next, we will introduce the category breakdown and the data collection process in detail.

heading: 3.1 Problem Categories
text: SafetyBench encompasses 7 categories of safety problems, derived from the 8 typical safety scenarios proposed by Sun et al. (2023). We slightly modify the definition of each category and exclude the Sensitive Topics category due to the potential divergence in answers for political issues in Chinese and English contexts. We aim to ensure the consistency of the test questions for both Chinese and English. Please refer to Appendix A for detailed explanations of the 7 considered safety issues shown in Figure 1.

heading: 3.2 Data Collection
text: In contrast to prior research such as Huang et al. (2023), we encounter challenges in acquiring a suf-
ficient volume of questions spanning seven distinct safety issue categories, directly from a wide array of examination sources. Furthermore, certain questions in exams are too conceptual, which are hard to reflect LLMs’ safety in diverse real-life scenarios. Based on the above considerations, we construct SafetyBench by collecting data from various sources including:
• Existing datasets. For some categories of safety issues such as Unfairness and Bias, there are existing public datasets that can be utilized. We construct multiple choice questions by applying some transformations on the samples in the existing datasets.
• Exams. There are also many suitable questions in safety-related exams that fall into several considered categories. For example, some questions in exams related to morality and law pertain to Illegal Activities and Ethics and Morality issues. We carefully curate a selection of these questions from such exams.
• Augmentation. Although a considerable number of questions can be collected from existing datasets and exams, there are still certain safety categories that lack sufficient data such as Privacy and Property. Manually creating questions from scratch is exceedingly challenging for annotators who are not experts in the targeted domain. Therefore, we resort to LLMs for data augmentation. The augmented samples are filtered and manually checked before added to SafetyBench.
The overall distribution of data sources is shown in Figure 3. Using a commercial translation API 1, we translate the gathered Chinese data into English, and the English data into Chinese, thereby ensuring uniformity of the questions in both languages. We also try to translate the data using ChatGPT that could bring more coherent translations, but there are two problems according to our observations: (1) ChatGPT may occasionally refuse to translate the text due to safety concerns. (2) ChatGPT might also modify an unsafe choice to a safe one after translation at times. Therefore, we finally select the Baidu API to translate our data. We acknowledge that the translation step might introduce some noises due to cultural nuances or variations in ex-
1https://fanyi-api.baidu.com/
pressions. Therefore, we make an effort to mitigate this issue, which will be introduced in Section 3.3.

heading: 3.2.1 Data from Existing Datasets
text: There are four categories of safety issues for which we utilize existing English and Chinese datasets, including Offensiveness, Unfairness and Bias, Physical Health and Ethics and Morality. Due to limited space, we put the detailed processing steps in Appendix B.

heading: 3.2.2 Data from Exams
text: We first broadly collect available online exam questions related to the considered 7 safety issues using search engines. We collect a total of about 600 questions across 7 categories of safety issues through this approach. Then we search for exam papers in a website 2 that integrates a large number of exam papers across various subjects. We collect about 500 middle school exam papers with the keywords “healthy and safety” and “morality and law”. According to initial observations, the questions in the collected exam papers cover 4 categories of safety issues, including Physical Health, Mental Health, Illegal Activities and Ethics and Morality. Therefore, we ask crowd workers to select suitable questions from the exam papers and assign each question to one of the 4 categories mentioned above. Additionally, we require workers to filter questions that are too conceptual (e.g., a question about the year in which a certain law was enacted) , in order to better reflect LLMs’ safety in real-life scenarios. Considering the original collected exam papers primarily consist of images, an OCR tool is first used to extract the textual questions. Workers need to correct typos in the questions and provide answers to the questions they are sure. When faced with questions that workers are uncertain about, we authors meticulously determine the correct answers through thorough research and extensive discussions. We finally amass approximately 2000 questions through this approach.

heading: 3.2.3 Data from Augmentation
text: After collecting data from existing datasets and exams, there are still several categories of safety issues that suffer from data deficiencies, including Mental Health, Illegal Activities and Privacy and Property. Considering the difficulties of requiring crowd workers to create diverse questions from
2https://www.zxxk.com/
scratch, we utilize powerful LLMs to generate various questions first, and then we employ manual verification and revision processes to refine these questions. Specifically, we use one-shot prompting to let ChatGPT generate questions pertaining to the designated category of safety issues. The incontext examples are randomly sampled from the questions found through search engines. Through initial attempts, we find that instructing ChatGPT to generate questions related to a large and coarse topic would lead to unsatisfactory diversity. Therefore, we further collect specific keywords about fine-grained sub-topics within each category of safety issues. Then we explicitly require ChatGPT to generate questions that are directly linked to some specific keyword. The detailed prompts are shown in Table 1.
After collecting the questions generated by ChatGPT, we first filter questions with highly overlapping content to ensure the BLEU-4 score between any two generated questions is smaller than 0.7. Than we manually check each question’s correctness. If a question contains errors, we either remove it or revise it to make it reasonable. We finally collect about 3500 questions through this approach.

heading: 3.3 Quality Control
text: We take great care to ensure that every question in SafetyBench undergoes thorough human validation. Data sourced from existing datasets inherently comes with annotations provided by human annotators. Data derived from exams and augmentation is meticulously reviewed either by our team or by a group of dedicated crowd workers. However, there
are still some errors related to translation, or the questions themselves. We find that 97% of 100 randomly sampled questions, where GPT-4 provides identical answers to those of humans, are correct. This eliminates the need to double-check these questions one by one. We thus only doublecheck the samples where GPT-4 fails to give the provided human answer. We remove the samples with clear translation problems and unreasonable options. We also remove the samples that might yield divergent answers due to varying cultural contexts. In instances where the question is sound but the provided answer is erroneous, we would rectify the incorrect answer. Each sample is checked by two authors at first. In cases where there is a disparity in their assessments, an additional author conducts a meticulous review to reach a consensus.

heading: 4 Experiments

heading: 4.1 Setup
text: We evaluate LLMs in both zero-shot and five-shot settings. In the five-shot setting, we meticulously
curate examples that comprehensively span various data sources and exhibit diverse answer distributions. Prompts used in both settings are shown in Figure 4. We extract the predicted answers from responses generated by LLMs through carefully designed rules. To let LLMs’ responses have desired formats and enable accurate extraction of the answers, we make some minor changes to the prompts shown in Figure 4 for some models, which are listed in Figure 5 in Appendix. We set the temperature to 0 when testing LLMs to minimize the variance brought by random sampling. For cases where we can’t extract one single answer from the LLM’s response, we randomly sample an option as the predicted answer. It is worth noting that instances where this approach is necessary typically constitute less than 1% of all questions, thus exerting minimal impact on the results.
We don’t include CoT-based evaluation because SafetyBench is less reasoning-intensive than benchmarks testing the model’s general capabilities such as C-Eval and AGIEval.

heading: 4.2 Evaluated Models
text: We evaluate a total of 25 popular LLMs, covering diverse organizations and scale of parameters, as detailed in Table 7 in Appendix. For API-based models, we evaluate the GPT series from OpenAI and some APIs provided by Chinese companies, due to limited access to other APIs. We also evaluate various representative open-sourced models.

heading: 4.3 Main Results
text: Zero-shot Results. We show the zero-shot results in Table 2. API-based LLMs generally achieve significantly higher accuracy than other open-sourced LLMs. In particular, GPT-4 stands out as it surpasses other evaluated LLMs by a substantial margin, boasting an impressive lead of nearly 10 percentage points over the second-best model, gpt-3.5-turbo. Notably, in certain categories of safety issues (e.g., Physical Health and Ethics and Morality), the gap between GPT-4 and other LLMs becomes even larger. This observation offers valuable guidance for determining the safety concerns that warrant particular attention in other models. We also take note of GPT-4’s relatively poorer performance in the Unfairness and Bias category compared to other categories. We thus manually examine the questions that GPT-4 provides wrong answers and find that GPT-4 may make wrong predictions due to a lack of understanding of certain words or events (such as “sugar mama” or the incident involving a stolen manhole cover that targets people from Henan Province in China). Related failing cases of GPT-4 are presented in Figure 6 in Appendix. Another common mistake made by GPT-4 is considering expressions containing objectively described discriminatory phenomena as expressing bias. These observations underscore the importance of possessing a robust semantic understanding ability as a fundamental prerequisite for ensuring the safety of LLMs. What’s more, by comparing LLMs’ performances on Chinese and English data, we find that LLMs created by Chinese organizations generally perform significantly better on Chinese data, while the GPT series from OpenAI exhibit more balanced performances on Chinese and English data.
Five-shot Results. The five-shot results are presented in Table 3. The improvement brought by incorporating few-shot examples varies for different LLMs, which is in line with previous observations (Huang et al., 2023). Some LLMs such as text-davinci-003 and internlm-chat-7B gain significant improvements from in-context examples, while some LLMs such as gpt-3.5-turbo might obtain negative gains from in-context examples. This may be due to the “alignment tax”, wherein alignment training potentially compromises the model’s proficiency in other areas such as in-context learning (Zhao et al., 2023). The impact of the selected 5-shot examples are discussed in
Appendix G.

heading: 4.4 Chinese Subset Results
text: Given that most APIs provided by Chinese companies implement strict filtering mechanisms to reject unsafe queries (such as those containing sensitive keywords), it becomes impractical to assess the performance of these API-based LLMs across the entire test set. Consequently, we opt to eliminate samples containing highly sensitive keywords and subsequently select 300 questions for each category, taking into account the API rate limits. This process results in a total of 2,100 questions. Considering the stability and API rate limits, we only conduct five-shot evaluation on this filtered subset of SafetyBench. As shown in Table 4. ChatGLM2 demonstrates impressive performance, with only about a three percentage point difference compared to GPT-4.

heading: 5 Discussion

heading: 5.1 Understanding vs. Generation
text: SafetyBench mainly measures LLMs’ ability to understand safety related issues, which leads to a natural question: What is the relationship between the safety understanding abilities measured in SafetyBench and the safety generation abilities? To answer this question, we transform some multiple-choice questions in SafetyBench to normal user queries. Specifically, we consider two types of queries: (1) constrained queries, which include constraints and the good responses are more limited; (2) open-ended queries, which have few constraints and the good responses are more distinct. We provide an example below: Original question: Question: To prevent your food from
drying out in the microwave, which of the following actions are safe to do?\n1. heat it up in the oven. ... 4. put a damp paper towel over the food.\nOptions: (A) 2, 3, 4 (B) 1, 2, 4 (C) 1, 2, 3 (D) 1, 3, 4
The transformed constrained query: I want to prevent food from drying out in the microwave, and I come up with a few solutions: \n1. heat it up in the oven. ... 4. put a damp paper towel over the food.\nWhich solutions do you think are good?
The transformed open-ended query: I want to prevent food from drying out in the microwave. What should I do?
We conduct experiments on six LLMs. We randomly sample 10 challenging Chinese questions for each category, ensuring that at least one of these models makes a wrong prediction. We then transform each question into a constrained query and an open-ended query. Afterward, we manually evaluate the safety of the models’ responses to these transformed queries. We present the accuracy for the original multiple-choice questions and the ratio of safe generations in Table 5. Note that as we only sample a subset, the comparative safety of various LLMs may deviate from those derived from the entire dataset. We could draw three important conclusions from the above results: (1) The safety defects reflected in SafetyBench could be exposed during generation. We could observe that the ratios of safe responses to constrained queries are similar to the accuracy on multiple-choice questions, suggesting that the identified safety understanding issues can contribute to unsafe generation. (2) The multiple-choice questions in SafetyBench are effective at identifying safety risks of LLMs. It is worth noting that the ratios of safe responses to open-ended queries are significantly higher than the accruacy on multiple-choice questions. According to our manual observation, this is because aligned LLMs tend to avoid the unsafe content considered in the multiple-choice questions when given an open-ended query. This suggests SafetyBench is effective at identifying the hidden safety risks of LLMs, which might be neglected if only open-ended queries are used. (3) The performances of LLMs on SafetyBench are correlated with their abilities to generate safe content. We find that the relative ranking of the six models is mostly consistent across all three metrics. What’s more, the system-level Pearson correlation between Accuracy and Constrained columns in Table 5 is 0.99, and the system-level Pearson correlation between Accuracy and Open-ended columns in Table 5 is 0.91, indicating a strong association between SafetyBench scores and safety generation abilities.
In summary, we argue that the measured safety understanding abilities in SafetyBench are correlated with safety generation abilities. Furthermore,
the safety defects identified in SafetyBench could be systematically exposed during generation.

heading: 5.2 Potential Bias for Data Augmentation with ChatGPT
text: To quantify the potential bias brought by employing ChatGPT for data augmentation, we compare the models’ performances on both augmented and original data across three categories. The results are shown in Table 6. From the results, we observe that ChatGPT does possess advantages in its augmented data, as indicated by the larger performance gap when evaluated on the augmented data compared to the original data. It is noteworthy, however, that this bias does not exert a significant influence on other models, including GPT-4. Therefore, we believe the impact of this bias is limited.

heading: 6 Conclusion
text: We introduce SafetyBench, the first comprehensive safety evaluation benchmark with multiple choice questions. With 11,435 Chinese and English questions covering 7 categories of safety issues in SafetyBench, we extensively evaluate the safety abilities of 25 LLMs from various organizations. We find that open-sourced LLMs exhibit a significant performance gap compared to GPT-4, indicating ample room for future improvements. We also show the measured safety understanding abilities in SafetyBench are correlated with safety generation abilities. We hope SafetyBench could play an important role in evaluating the safety of LLMs and facilitating the rapid development of safer LLMs.

heading: Acknowledgement
text: This work was supported by the NSFC projects (Key project with No. 61936010). This work was supported by the National Science Foundation for Distinguished Young Scholars (with No. 62125604).
Limitations
While we have amassed questions encompassing seven distinct categories of safety concerns, it is important to acknowledge the possibility of overlooking certain safety concerns, such as political issues. Furthermore, in our pursuit of striking a balance between comprehensive problem coverage and efficient testing, we have assembled a total of 11,435 multiple-choice questions. This collection
allows for the evaluation of LLMs with an acceptable cost. Nonetheless, we acknowledge that due to the limited number of questions, certain topics may not receive adequate coverage.
During data augmentation, we use ChatGPT to generate new questions through few-shot prompting, which might make it advantageous for ChatGPT. We quantify the brought potential bias in Section 5.2. The conclusion is that ChatGPT does possess advantages in its augmented data, while this bias does not exert a significant influence on other models, including GPT-4. Therefore, we believe the impact of this bias is limited.
As the first effort to compile a large and comprehensive safety evaluation benchmark with multiplechoice questions, we argue that the current level of data difficulty is acceptable, given that the overall scores for 22 out of the 25 evaluated LLMs are consistently less than 80%. What’s more, it is noteworthy that the absolute number of challenging questions is considerable (>1K for GPT-4 and >2K for most open-sourced LLMs). Therefore, one straightforward approach to make SafetyBench seem more challenging is to remove some easy questions that most LLMs get right, which could also retain a considerable number of total questions. Based on these reasons, we believe SafetyBench is challenging enough. However, we do agree that it is a good idea to collect more challenging multiple-choice questions in future work.
Ethical Considerations
Based on manual inspection, SafetyBench contains no personal information, thus guaranteeing the absence of privacy information leaks. Furthermore, SafetyBench does not incorporate adversarial prompts that could provoke detrimental responses from LLMs, making it challenging for potential attackers to exploit the questions in SafetyBench to hack LLMs and induce harmful outputs.
When collecting data from exams, we inform the crowd workers in advance how the annotated data will be used. We pay them about 22 USD per hour, which is higher than the average wage of the local residents.

heading: A Explanations of Considered Safety Issues
text: The explanations for the 7 considered safety issues are listed as follows:
1. Offensiveness. This category is about threat, insult, scorn, profanity, sarcasm, impoliteness, etc. LLMs are required to identify and oppose these offensive contents or actions.
2. Unfairness and Bias. This type of safety problem is mainly about social bias across various topics such as race, gender, religion, etc. LLMs are expected to identify and avoid unfair and biased expressions and actions.
3. Physical Health. This category focuses on actions or expressions that may influence human physical health. LLMs should know appropriate actions or expressions in various scenarios to maintain physical health.
4. Mental Health. Different from physical health, this category pays more attention to health issues related to psychology, spirit, emotions, mentality, etc. LLMs should know correct ways to maintain mental health and prevent any adverse impacts on the mental well-being of individuals.
5. Illegal Activities. This category focuses on illegal behaviors, which could cause negative
societal repercussions. LLMs need to distinguish between legal and illegal behaviors and have basic knowledge of law.
6. Ethics and Morality. Besides behaviors that clearly violate the law, there are also many other activities that are immoral. This category focuses on morally related issues. LLMs should have a high level of ethics and be object to unethical behaviors or speeches.
7. Privacy and Property. This category concentrates on the issues related to privacy, property, investment, etc. LLMs should possess a keen understanding of privacy and property, with a commitment to preventing any inadvertent breaches of user privacy or loss of property.

heading: B Detailed Steps for Collecting Data from Existing Datasets
text: Offensiveness. The employed Chinese datasets include COLD (Deng et al., 2022). COLD is a benchmark for Chinese offensive language detection. It comprises posts from social media platforms that are labeled as offensive or not by human annotators. We randomly sample a total of 288 instances labeled as Attack Individual and 312 instances labeled as Other Non-Offensive to construct questions with two options, which require to judge whether the provided text is offensive. The employed English datasets include the Jigsaw Toxicity Severity dataset 3 and the adversarial dataset proposed in Dinan et al. (2019). The Jigsaw Toxicity Severity dataset comprises pairs of Wikipedia Talk page comments, with annotations identifying the more toxic comment in each pair. We randomly sample 700 pairs of comments to construct the questions which require to choose the more toxic option. The adversarial dataset proposed in Dinan et al. (2019) is collected by encouraging annotators to hack trained classifiers in a multi-turn dialogue. We randomly sample 350 safe responses and 350 unsafe responses to construct the questions, which entail the evaluation of the offensiveness of the last response in a multi-turn dialogue.
Unfairness and Bias. The employed Chinese datasets include COLD and CDial-Bias (Zhou et al., 2022). We randomly sample 225 instances that are labeled as Attack Group and 225 instances that are
3https://www.kaggle.com/competitions/ jigsaw-toxic-severity-rating/overview
labeled as Anti-Bias. The sampled instances are uniformly drawn from three topics including region, gender and race. Note that there is no overlap between the COLD data used here and the COLD data used in the Offensiveness category. CDial-Bias is another Chinese benchmark focusing on social bias, which collects data from a Chinese questionand-reply website Zhihu 4. Similarly, we randomly sample 300 biased instances and 300 non-biased instances uniformly from four topics including race, gender, region and occupation. The employed English datasets include RedditBias (Barikeri et al., 2021). RedditBias gathers comments from Reddit and annotates whether the comments are biased. We randomly sample 500 biased instances and 500 non-biased instances uniformly from five topics including black person, Jews, Muslims, LGBTQ and female. We employ samples from COLD, CDialBias, and RedditBias to create two-choice questions that assess whether a given text exhibits bias or unfairness.
Physical Health. We haven’t found suitable Chinese datasets for this category, so we only adopt one English dataset: SafeText (Levy et al., 2022). SafeText contains 367 human-written real-life scenarios and provides several safe and unsafe suggestions for each scenario. We construct two types of
4https://www.zhihu.com/
questions from SafeText. The first type of question requires selecting all safe actions among the mixture of safe and unsafe actions for one specific scenario. The second type of questions requires comparing two candidate actions conditioned on one scenario and choosing the safer action. There are 367 questions for each type.
Ethics and Morality. We haven’t found suitable Chinese datasets for this category, so we only employ several English datasets including Scruples (Lourie et al., 2021), MIC (Ziems et al., 2022), Moral Stories (Emelin et al., 2021) and Ethics (Hendrycks et al., 2021a). Scruples pair different actions and let crowd workers identify the more ethical action. We randomly sample 200 pairs of actions from Scruples to construct the questions requiring selecting the more ethical option. MIC collect several dialogue models’ responses to prompts from Reddit. Annotators are instructed to judge whether the response violates some Rule-ofThumbs (RoTs). If so, an additional appropriate response needs to be provided. We thus randomly sample 200 prompts from MIC, each accompanied by both an ethical and an unethical response. The constructed questions require identifying the more ethical response conditioned on the given prompt. Moral Stories include many stories that have descriptions of situations, intentions of the actor, and
a pair of moral and immoral action. We randomly sample 200 stories to construct the questions that require selecting the more ethical action to achieve the actor’s intention in various situations. Ethics contains annotated moral judgements about diverse text scenarios. We randomly sample 200 instances from both the justice and the commonsense subset of Ethics. The questions constructed from justice require selecting all statements that have no conflict with justice among 4 statements. The questions constructed from commonsense ask for common-
sense moral judgements on various scenarios.

heading: C Evaluation Prompts
text: The default evaluation prompts are shown in Figure 4. However, we observe that conditioned on the default prompts, some LLMs might generate responses that have undesired formats, which makes it hard to automatically extract the predicted answers. Therefore, we make minor changes to the default prompts when evaluating some LLMs, as detailed in Figure 5.

heading: D Evaluated Models
text: The detailed information of 25 evaluated LLMs are shown in Table 7.

heading: E Failing Cases
text: We show one failing case of GPT-4 for each safety category in Figure 6.

heading: F Examples
text: We present two example questions for each safety category in Figure 7.
G Impact of the Selected 5-shot Examples
To explore the impact of the 5-shot examples, we employ a random sampling approach to create two distinct groups of 5-shot examples from the existing Chinese test set. Including the initial 5-shot examples, we have three sets of distinct 5-shot examples for each category. The selected examples are excluded from the test set. Then we evaluate the models using distinct 5-shot examples three times. The results are shown in Table 8. We observe that the selected examples exert a small influence on the overall performance, as evidenced by the small standard deviation. Notably, certain categories, such as OFF and UB, exhibit a relatively larger standard deviation. This could be attributed to the possibility that the models are more susceptible to the safety standards reflected in the examples associated with these specific categories.

heading: Future Work
text: However, we do agree that it is a good idea to collect more challenging multiple-choice questions in future work. Ethical Considerations
Based on manual inspection, SafetyBench contains no personal information, thus guaranteeing the absence of privacy information leaks. Furthermore, SafetyBench does not incorporate adversarial prompts that could provoke detrimental responses from LLMs, making it challenging for potential attackers to exploit the questions in SafetyBench to hack LLMs and induce harmful outputs. When collecting data from exams, we inform the crowd workers in advance how the annotated data will be used. We pay them about 22 USD per hour, which is higher than the average wage of the local residents