InstructSafety: A Unified Framework for Building Multidimensional and Explainable Safety Detector through Instruction Tuning
Authors: Zhexin Zhang
Jiale Cheng
Hao Sun
Jiawen Deng
Minlie Huang
Venue: EMNLP 2023 Findings (2023)
Keywords: safety detection
unified framework
instruction tuning
TL;DR: A unified framework to build multidimensional and explainable safety detector through instruction tuning.

Abstract: Safety detection has been an increasingly important topic in recent years and it has become even more necessary to develop reliable safety detection systems with the rapid development of large language models. However, currently available safety detection systems have limitations in terms of their versatility and interpretability. In this paper, we first introduce InstructSafety, a safety detection framework that unifies 7 common sub-tasks for safety detection. These tasks are unified into a similar form through different instructions. We then conduct a comprehensive survey of existing safety detection datasets and process 39 human-annotated datasets for instruction tuning. We also construct adversarial samples to enhance the model's robustness. After fine-tuning Flan-T5 on the collected data, we have developed Safety-Flan-T5, a multidimensional and explainable safety detector. We conduct comprehensive experiments on a variety of datasets and tasks, and demonstrate the strong performance of Safety-Flan-T5 in comparison to supervised baselines and served APIs (Perspective API, ChatGPT and InstructGPT). We will release the processed data, fine-tuned Safety-Flan-T5 and related code for public use.

INSTRUCTSAFETY: A Unified Framework for Building Multidimensional and Explainable Safety Detector through Instruction Tuning
WARNING: This paper contains some examples which are offensive in nature.
Zhexin Zhang, Jiale Cheng, Hao Sun, Jiawen Deng, Minlie Huang"
The CoAl group, DCST: Institute for Artificial Intelligence; State Key Lab of Intelligent Technology and Systems; Beijing National Research Center Information Science and Technology: Tsinghua University, Beijing 100084, China.
2x 2x-zhang22emails tsinghua. .dw..edddccoo cn, aihuang@ts inghua..nghca..m .ddu.cn
Abstract
Ben is Irind teacher but he is nir
Safety detection has been an increasingly im- portant topic in recent years and it has become even more necessary to develop reliable safety detection systems with the rapid development of large language models. However, currently available safety detection systems have limi- tations in terms of their versatility and inter- pretability. In this paper, we first introduce INSTRUCTSAFETY
Safety-Hlan-TTs
Sade?
Problem??
Lewd?
Target?
Ratsonale?
Explanation?
Blased
Black
It implies being black is thing.
No
ni
opánions
people
safety detection frame- work that unifies 7 common sub-tasks for safety detection. These tasks are unified into a sim- ilar form through different instructions. We then conduct comprehensive survey of ex- isting safety detection datasets and process 39 human-annotated datasets for instruction tuning. We also construct adversarial sam- ples enhance the model's robustness, ter fine-tuning Flan-T5 the collected data, we have developed Safety-Flan-IT a multi- dimensional and explainable safety detector. We conduct comprehensive experiments on a variety of datasets and tasks, and demon- strate the strong performance of Safety-Flan- T5 in comparison to supervised baselines and served APTs (Perspective API, ChatGPT In- structGPT). Our Github repository is at https: //gi thub. http thu hhw.:oooaaiiinttuussssaett
Figure I: An example of Safety-Flan-TS. Given the input text, Safety-Flan-T5 can analyze the safety of the text from multiple dimensions and provide explana- tions. The "Lewd" in the figure can be replaced with any other specific safety problems. Safety-Flan- can also compare the safety/faimnessmorraity of several options, which is not shown in this figure.
of fine-grained detection ability. Some safety de- tectors only support binary classification and oth- ers can detect a limited range of safety issues (e.g.. some safety issues related to hate speech). (2) Lack of interpretability. Most of the safety detectors can only provide numerical scores as the judging confidence while lacking the ability to offer more explainable information such rationales or rea- described in natural language. (3) Lack of context-sensitive detection ability. Many safety detectors cannot handle multiple rounds conver- sations, which has been a mainstream human-Al interaction scenario. (4) Lack of generalization ability. The majority of current safety detectors are trained on data from limited sources, posing challenge for them to correctly handle out-of- distribution (OOD) data.
1 Introduction
Safety detection has gained increased attention in recent years (Xu et al., 2020; Barikeri et al., 2021; Sun et al., 2022), which is important in many sce- narios (e.g., detecting unsafe comments in online forums or under public videos). Due to the rapid development of large language models and the growing amount of human-AI interactions, it is becoming increasingly necessary to develop effec- tive safety detection systems that can ensure safe and reliable interactions between humans and Al. However, currently available safety detectors (e.g., Perspective API) have several limitations: (1) Lack
To address the above limitations of existing safety detection systems, we propose INSTRUCT- AFETY, a unified framework that includes 7 com- mon sub-tasks for safety detection Specifically,
'Our focus lies in detecting safety concerns within the text itsclf, and the text could come from humans or models.
"Corresponding author.

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we utilize the popular instruction tuning (Sanh al., 2022) paradigm to train a unified model that could handle different safety detection tasks simultaneously. Furthermore, in order to make the model cover a wide range of safety issues, we comprchensively survey the existing safety-related datasets and finally obtain 39 gumannannnoatted datasets. Afterwards, we utilize diverse templates convert the samples into a uniform format for instruction tuning. To enhance the robustness of the model, we augment the training data with auto- matically generated adversarial instances that asso- ciate with specific groups of people who may face discrimination. After fine-tuning Flan-T5 (Chung et al., 2022) on the augmented data, we obtain a multidimensional and explainable safety detector, named Rafety--Fan--S. We show example of Safety-Flan-TS for 6 sub-tasks in Figure 1.
process 39 datasets for instruction tuning.
We propose simple but effective method to augment adversarial samples that can improve the model's 's robustness. With the augmented data, we train Safety-Flan- -aay---la--S,,, T5, a multidimen- sional and explainable safety detector.
Comprchensive experiments demonstrate the strong performance of Safety-Flan-TS on the 7 tasks and the superiority of Safety-Flan- compared with P-API, the most popular free safety detection system. We also demonstrate the first attempt to evaluate gpt-3.5-turbo and text-davinci-003s many sub-tasks of safety detection, which further verifies the versatility and generalization ablity of Safety-Flan Saant---Fa---S--
Related Work
We evaluate Safety-Flant T5 on variety of datasets, covering the proposed 7 sub-tasks, with both zero-shot and full-shot settings. Experiments show that Safety-Flan-- can achieve competitive performance in all 7 tasks compared with strong baselines. In particular, compared with Perspec- tive API (P-API), widely used free safety detec- tor, Safety-Flan-T5 exhibits better generalization ability the zero-shot experiments and supports a wider range of safety detection tasks. In addition, to the best of our knowledge, we are the first to evalu- ate ChatGPT (gpt-3.5-turi) d on variety of sub-tasks for safety detection. According to the results, Safety-Flan- T5 outperforms gpt-3.5-turbo and text-davinci-003 across various datasets in both zero-shot and full- shot scenarios, although they have been reported surpass supervised baselines and even human performance certain tasks such as information extraction (Wei et al., 2023) and stance detection (Gilardi et al., 2023). Moreover, we conduct abla- tion studies to validate the efficacy of our approach and show some representative cases to facilitate better understanding of the model's strengths and weaknesses.
2.1 Safety Detection
According to Deng et al. (2023), main safety is- sues can be divided into categories: (I) abusive and toxic contents (Poletto al., 2021; Schmidt and Wiegand, 2017: Davidson et al.. 2017). (2) un- fairness and discrimination (Barikeri et al., 2021; Nangia et al., 2020; Sap et al., 2020; Dhamala et al., 2021), (3) ethics and morality issues (Lourie et al., 2021: Hendrycks et al., 2021; Forbes et al.. 2020; Jiang et al., 2021), (4) risk of misleading and privacy information (Carlini al., 2021; Pan et al., 2020; Carlini et al., 2019; Bang al., 2021a; Zhang et al., 2023b). In recent years, many datasets have been created to help detect these safety issues (Levy et al., 2022; Sun et al., 2022; Zampieri et al., 2019; Zhang et al., 2022b, 2023a). While most of the datasets focus on certain parts of the overall safety issues, we make an effort to aggregate the datasets together, which helps to build more gen- eralized safety detector. As for the model for safety detection, many works (Lourie al., 2021: Sun et al., 2022: Xu et al., 2020) employ the classifi cation based models such as BERT (Devlin et al., 2019), while we use the generation based model with instruction tuning, which enables our model to provide multidimensional judgements and have het- ter explainability. Considering the strong language modeling ability of GPT3.5 and GPT4, some works are starting explore using GPT3.5 and GPT4 the evaluator (Luo et al., 2023; Liu et al., 2023; Wang et al., 2023; Gilardi et al., 2023), while we are the first to explore using GPT3.5 as the safety
and InstructGPT (text-
In summary, our contributions are threefold.
We introduce INSTRUCTSAFETY, unified framework that integrates common sub- tasks of safety detection based on different instructions. Additionally, we throughly sur- vey the existing safety detection datasets and
'nttps://platorma openai. com/docss/mdels.. gpt-3-5

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detector on a variety of safety detection tasks the best of our knowledge.
3.2 Data Collection
We conduct a thorough survey of the available safety detection datasets, and to ensure the data's high quality, we only consider datasets annotated by human workers, rather than automatic models, We assign different task categories to each dataset based on their respective annotations. The com- plete list of datasets used for each task is shown in Table 1. To keep the instructions diverse, we man- ually write several different instructions for each task and each sample will be assigned randomly selected instruction. Please refer to Appendix A for detailed instructions.
2.2 Instruction Tuning
Instruction tuning is recent popular paradigm where models are trained on many tasks with dif- ferent instructions. The main goal of instruction tuning is to endow the model with the ability to follow instructions (maybe unseen during training). TO (Sanh et al., 2022). Taan-T5/FFan-PLL.M (Chung et al., 2022) and Tk-Instruct (Wang et al., 2022) are some examples of instruction tuned models, which are trained on thousands of academic bench- marks. However, we note that there are few tasks specific for safety when training these popular in- struction tuned models, which limit their abilities to directly serve as good safety detectors. There- fore, we summarize common safety detection tasks and collect relevant datasets to empower the gen- erally instruction-tung models perform safety detection more effectively.
For the Problem Detection task, it is optional to provide a list of all possible problems in the instruc- tion. If the list is not provided, we let the model choose from default list of problems summarized from the collected datasets: (I) Stereotypes, dis- crimination or biased opinions. (2) Lewdness. (3) Aggression (including threat, sarcasm, etc.). (4) Insult. (5) Sensitive topics (e.g., politics, drugs, physical health, etc.). (6) Immorality or illegal- ity. In this way, our model has the flexibility to give a reasonable output regardless of whether the instruction includes a list of all possible problems.
3 Method
3.1 Framework
3.3 Instruction Tuning
The INSTRUCTSAFETY framework is shown in Fig- ure 2. We summarize seven common sub-tasks for safety detection: (1) Safety Judgement: Judge the overall safety of the input text (i.e., whether the text is fair, ethical, polite, etc.). (2) Problem Detection: Detect and list all possible safety prob- lems of the input text. (3) Problem Judgement: Judge whether the input text exhibits some spe- cific problem (e.g., immoral, lewd, unfair, etc.). (4) Safety Comparison: Compare the safety of two more options and decide which option is more favorable. (5) Target Detection: Detect the target groups of people that might be attacked in the input text. (6) Span Detection: Detect those spans (i.e., a succession of tokens) the input text that might cause safety problems. (7) Safety Explanation: Explain why the input text is unsafe using natural language. These seven tasks can be unified into the following form: Instruction + Text + Output. For sub-tasks (2), (5), (6), and (7), the output is set "None" for safe input text. Note that the input text can either be standalone text with no inter- action or a dialogue comprising multiple rounds interaction, which enables the model to perform context-sensitive safety detection.
As we rely instruction tuning to learn multiple tasks simultaneously, expect the base model to have the ability of following instructions, which may be beneficial for our training. Therefore, we select Flan-T5 as our base model, a recent powerful encoder-decoder model trained on a variety of tasks with instructions. During training, the instruction and the input text are fed into the encoder, and the decoder is required to produce the accurate output. Note that samples for different tasks are mixed together. We optimize the model using the standard cross entropy loss.
3,4 Robustness Enhancement
After fine-tuning Safety-Flan-Ts, in our pilot exper- iments, we observe that the model has a strong ten- dency to falsely predict those sentences including certain groups of people as unsafe. For example, the sentence "His teacher is woman" would be predicted as unsafe by initially trained Safety-Flan- T5. We assume the bias may be brought by the collected datasets (i.e., many of the samples con- taining these groups are labeled as unsafe in the training set). The similar problem is also observed

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Safety Judgement
Problem Detection
Problem Judgement
Safety Comparison
Target Detection
Span Detection
Safety Explanation
Please list the possible safety, fairness and morality problems in the following text.
Whichh of the fallowing options more tonác, unfair, immoral and umreasonable?
fallowing text safe, fair, ethical and reasonable?
Is following immoral?
Who attacded following text?
What parts of text risk safety, fairmess und morality?
Please explain the reason if the following response is unsafe, unfair immoral.
Instruction
I do the maslim terrorist as you are putting my life in danger with your politics would
going
Text
(A) breaking my friend's is earbeds
shut down an all girls club school. Think that's bad thing to do?
Talking badly about someone bahind their back.
Duman rights tribunals rival the Nazis.
Are talking about sund niggers?
me to the rohbery, I'll kill you.
(R) calling 911
You should not stup the club, unless they are doing something wrong.
report someone might have attempted
Safely- Flan-T5
like to suggest that you all go fack yourselves
Response: A: No. but think they wasting, tuition
suicide
Aggreasion; Immorrllity or illegality.
hands of muslim terrorist (2) fuck yourselves
It's wrong gender
Output
No.
Yes.
(A).
Arab.
justify denying otherss
rights.
Figure 2: The INSTRUCTS AFETY framework. We design several different instructions for each of the sub-tasks for safety detection and we show one example instruction for each sub-task. The concatenated instruction and text are fed into Safety-Flan-TS to produce the final output.
Task
# Datuset
Dutasets
Safety Judgement
32
RedditBias (Barikeri et al., 2021), AdHominTTeed (Sheng et al., 2021). MDMD (Zhang et al., 2022a), StereoSeet (Nadeem et 2021), TalkDown (Wang and Potis, 2019), PersonalAttack (Wulezyn et al., 2017). AwitterAbusive (Founta et al., 2018), Unhealthy Comment Corpus (Price al., 2020), Contex(Toxicity (Pavlopoulls et al., 2020), HatEval (Basile al., 2019), FoxNews (Gao Huang. 2017), ToxiChat (Baheti (Dinan et al., 2019), Tweetblm (Kumar and Pranesh. 2021), Hate Speoch Dataset (de Gibert et al., 2018), Gab Hate Corpus (Kennedy et al.). Latent Hatred (EIShericf et 2021), Dynamically Generated Dataset (Vidgen et al., 2021), Hate Speech and Offeusive Language (Davidson et al., 2017 Jigsaw Toxic Commeat Classification Jigsaw Unintended
2021), BreadBraa
(Zampsari al. 2019), DiaSafety (San al., 2022), SafeText (Levy al., 2022), BAD (Xu et al., 2020), ProsocialDialog (Kim et al., 2022), MIC (Ziems et al., 2022), SOCIAL --EE--CHEM-------- 101 (Forbes 2020). Commonsense Norm (Hendrycks ol., 2021), SBIC (Sap et al., 2020).
(Jiang al., 2021), Xplain (Mathew al., 2021),
Problem Detection
28 Micronggression Dataset (Breitfeller et al., 2019), LaHoomlTTweete Political Prudence (Mang et al., 2021b), MDMD, SterenSer, Down, PersomalAttack, Ahusive BAD, Unhealthy Comment Corpus, HatEval, FewNews, ToxiChat, Tweetblm, Hate Speech Dataset, Gab Hate Corpus, I Latent Hatred, Dynamically Generated Dataset, Hate Speech and Offensive Language, Jigsaw Toxic Comment Classification, Jigsaw Unintended Bias, HateXplain, SBIC.
OLID, DinSufety, SafeText, MIC, SOCIAL-CHEM-
Problem Judgement
13
SACIAL-CHEM-10 AdHomlnTTwedd StereoSet, Towk, FoxNews, ToxiChat, Tweethim, Hate Speech Dataset, Dynumically Generated Dataset, OLJD, SafeText, MIC,
Safety Comparison
Pains (Nangia MIC, Moral Stories (Emelin et al., 2021), Ethics.
Scruples et al., 2021),
SterooSet, Jigsaw Toxicity Severity, Safe Text.
Target Detection
9
RedditBias, StereoSet. HatEval, Lateat Hatred, Dynamically Generated Dataset, Jigsaw Unintended Biss, OLID. HateXplain, SBIC.
Span Detection
3
TalkDown, Toxic Spans (Pavlopoulos al., 2022), HateXplain.
Safety Explanation
5
ProsocialDialog, Latent Hatred, MIC, Moral Stories, SBIC.
Table 1: List of datasets used for each task.
in popular safety detection systems such as P-API (Hutchinson et al., 2020).
some group g in 9 with the following instruc- tions given to gpt-3.5-turif and text-davinci- 003:
To mitigate the bias, we utilize gpt-3.5-turbb and (ext-davinci-003, two powerful models served by OpenAl, to automatically generate safe sentences that include certain groups of people. We divide the robustness enhancement process into four steps:
Instruction [1/2/3/4]: Please write 5 [ first person/ second person/ third person] descriptions that include [g) that might appear in dialog:
3. Generate descriptions with fixed patterns in- cluding some group g in & with the following instructions given to gpt-3.5-turbo and text- davinci-003:
1. Collect list of groups G that might be dis- criminated against. The list has 24 groups and the detailed list is shown in Appendix C.
2. Generate free-form descriptions including
Instruction:
Pleuse write sentences that have similar

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sentence patterns "His teacher (g)", "Her colleague [g]" or "The singer (g)":
Task
#Dataset
#Sample
#Samplede>
Sufety Judgement
10,000
32
305,980
115,677
Problem Detection
11,000
28
179.431
54,709
Note that we put different articles in front of different groups g.
Problem Judgement
12,000
13
112.720
38,089
Safety Comparison
18,000
8
77.208
5,491
Target Detection
15,000
9
75.629
11,254
Span Detection
30,000
3
17.210
1,439
4. Use the fine-tuned Safety-Flan -lan---- to iden- tify any generated descriptions that are falsely predicted as unsafe, and incorporate these de- scriptions into training set for the safety judgement task.
Safety Explanation
20,000
76,504
Total
39
844,682
250,783
Table 2: Detailed statistics of the collected datasets after sampling.
We finally collect 210 descriptions with fixed pat- terns and 3153 free-form descriptions which are randomly inserted into our training set. We ran- domly sample 100 added descriptions and find that 99% of them are actually safe according to careful examination.
of the safety detectors. Besides the baselines re- ported Röttger et al. (2021), we also compare zero-shot S-Flan-T5 with zero-shot gpt-3.5-turbor text-davinci-OM and Flan-T5. We set the tempera- ture to 0 when testing gpt-3.5-turbo, text-davinci- 003 and Flan-T5 in all of our experiments to obtain deterministic results. Please refer to Appendix B for all prompts used for testing gpt-3.5-turbo, text- davinci-003 and Flan-T5 in our experiments. The result is presented Table 3, which indicates that S-Flan-T5 attains the highest accuracy for both non-hateful and all samples. Other strong models such as P-API and gpt-3.5-turbo, however, achieve noticeably lower accuracy non-hatefur samples, indicating higher likelihood of falsely predict- ing sentences containing sensitive words as unsafe. What's more, S-Flan-TS exhibits significantly su- perior performance compared to its base model, T5an-T5. This observation underscores the impor- tance of instruction tuning on the processed data.
4 Experiments
4.1 Setup
Training & Inference During inference, we adopt greedy sampling to make the generation pro- cess deterministic. More details are in Appendix D.
Data Since the number of samples for each sub- task is unbalanced, we randomly sample at most N instances for each pair of (dataset, sub-task) to construct the final dataset, following Gupta al. (2022). Note that we set a higher N for the sub- task that has fewer instances. Detailed statistics are shown in Table 2. We also randomly sample 10,000 instances from the training set and calculate their Distinct-4 value (the proportion of distinct 4- grams) to measure the diversity. The metric yields a value of 0.66 when instructions are included and 0.87 when excluding the instructions manually write for seven sub-tasks.
Model
AcChatetul
Ace hateful
AcCutal
BERT-D
75.5
36.0
63.2
BERT-F" P-API
65.5
48.5
60.2
89.5
48.2
76.6
SN!
86.6
33.2
4.2
Zero-Shot Experiments
gpt-3.5-turbo
99.8
68.4
90.0
fext-davinci-00s
100.0
23.6
76.1
In this section, we will introduce the results for zero-shot experiments.
Flan-T5/ang
97.8
11.8
70.9
Flan-T5xx.
99.9
28.9
77.7
Flan-T5xxL
99.1
40.9
80.9
4.2.1 Safety Judgement
S-Flan-T5hare
97.6
84.3
93.4
S-Flan-T5xL S-Flan-T5yxt 98.6
98.4
89.1
95.5
To evaluate the robustness of S-Flan-Ts -5 on judging the overall safety of out-of-domain data, we eval- uate its zero-shot performance the HatcCheck dataset (Röttger et al., 2021), which includes com- prehensive functional test cases for hate speech de- tection. Many non-hateful sentences in the dataset may include some sensitive words such as "gay person", which can be useful to test the robustness
91.1
96.3
Table 3: Results of safety judgement task the Hate- Check dataset. The best performance is highlighted bold, and the second best underlined. All results are multiplied by 100. represents the result is reported in Röttger et al. (2021).

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4.2.2 Problem Judgement
initially randomly sample 20,000 test cases. Then we randomly filter out 15,000 safe cases as the test set contains roughly 90% safe cases. The re- maining 5,000 test cases have a reasonable and balanced ratio of about 60% safe cases. We com- pare S-Flan-T5 with P-API and few-shot Flan-T5, gpt-3.5-turbo and text-davinci-003. Two examples including safe one and an unsafe one used the demonstration when evaluating Flan-T5, gpt- 3.5-turbo and text-davinci-OO5, As shown Table 5, S-Flan-TT achieves the best performance on all three metrics. According to the P-API website, the Jigsaw team uses the Jigsaw Toxic Comment Clas- sification dataset to train P-API. This implies that the test of P-API is within its designated domain. The comparatively lower performance of gpt-3.5- turbo and text-davinci-000 suggests that detecting fine-grained safety issues is not trivial task for them. Improvements might be achieved through prompt engineering or other methods, which is out of the scope of this work. Once again, S-Flan-TT surpasses Flan-T5 by a significant margin, high- lighting the crucial role of our instruction tuning.
To assess the generalization ability of S-Flan-T5 in judging some safery issues that are not encountered during training, we evaluate "zero-shot" S-Flan-T5 on the DiaSafety dataset (Sun et al., 2022), which defines context-level safety problems including offending user (OU), risk ignorance (RT), unau- thorized expertise (UE), toxicity agreement (TA) and biased opinion (BO). We call it "zero-shot" be- cause we use the DiaSafety dataset during training for other sub-tasks instead of the problem judge- ment task. We provide a brief explanation for each safety problem the prompt (listed in Appendix B) when testing S-Flan-T3, Flan-T5, gpt-3.5-turbo and text-davinci-000 As shown in Table 4, S-Flan- T5 exhibits comparable results to the fine-tuned RoBERTa, which suggests the strong context-level ability and generalization capacity of --5.. It is worth noting that Flan-T5,, gpt-3.5-turbo and text-davinci-003 perform relatively poorly on this task, which indicates the challenge identifying context-level safety problems.
Model
UE
Avg.
RoBERTa
85.2
77.2
94.5
94.5
66.0
83,5
Model
Acc
Micro-F1
Macro-F1
gpt-3.5-turbo
66.7
62.9
36.7
26.0
49.7
48.4
P-API
63.6
74.5
66.5
text-davinci-003
43.6
9.6
11.7
6.5
39.8
22.2
gpt-3,5-turbo
51.9
50,3
47.0
57.6
31.6
43.6
63.3
39.1
47.1
text-davinci-003
57.6
61.6
55.6
Flan-T5xx.
57.9
18.5
44.4
25.0
15.4
32.2
Flan-TStare
49.3
2.1
2.6
Flan-T5xxI.
77.4
36.2
63.7
59.6
54.2
58.2
T1xn-T5xx.
57.3
24.2
25.5
S-Flan-T5tarr
77.5
67.1
89.9
89.7
68.7
78.6
Vlan-T5xxI.
53.6
7.9
12.6
S-Flan-T5xx
77.7
78.3
93.3
90.3
65.7
81.1
S-Flan- -5yxt
80.0
80.6
94.4
93.1
66.7
83.0
-llan-TStage
72.1
S-Flan-T5xL
72.9
72.9
Table 4: Results (FI score) of problem judgement task on the DiaSafety dataset, All results are multiplied by 100. represents the result in Sun et al. (2022).
S-Flan-T5xww 73.0
73.8
Table 5: Results of problem detection task on the Jigsaw Toxic Comment Classification dataset. All results are multiplied by 100.
4.3 Full-Shot Experiments
In this section, we will introduce the results for full-shot experiments.
4.3.2 Safety Comparison
We evaluate full-shot S-Flan-TS on the Scruples dataset (Lourie et al., 2021), which contains dilem- mas. Models need to choose the option that aligns better with ethical principles when given two morally related actions. We don't don't include the gpt-3.5-turde result because it often refuses to an- swer the safcty comparison question. Based on the result presented in Table 6, S-Flan-T5 demon- strates superior performance in selecting safer and more ethical choices. This implies that S-Flan-T5 better aligned with the human values reflected the Scruples dataset.
4.3.1 Problem Detection
We evaluate full-shot S-Flan-T5 on the Jigsaw Toxic Comment Classification dataset 3, which in- cludes labels for different toxicity categories assoc- itated with each comment. Note that we only use part of the training set (N=11,000 for problem de- tection task). We evaluate the performance on five categories including toxic, obscene, identity hate, insult and threat. To reduce the testing costs, we
htt //www. kaggle, con/c/ ji gsaws toxit-o-coooooooo www.eotoomooooooooo wowedc-coomet--llsssfiiiite leanee

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Model
Macro-Fled
Macro-FIuu
Model
FOU
Token F1
BERT!
0.728
0.720
BiRNN!
0.222
0.506
RoBERTa
0.757
0.746
BERT
0.130
0.497
text-davinci-003
0.602
0,631
gpt-3.5-turbo
0.335
0.517
Laaage
0.521
0.547
text-davinci-003
0.363
0,565
Flan-T5xi.
0.591
0.607
Flan-TStarge
0.171
0.402
Flan-T5xxL
0.664
Flan-T5xx T5xL.
0.467
Flan-T5xa
0.221
0.447
S-Flan-TSuage
0.734
0.733
S-Flan-TSxi.
0.756
0.761
S-Flan-T31arg
0.553
0.703
S-Flan-T5xxi
0.764
0.763
S-Flan-T5xi. 0.553
0.706
S-Flan-T5xy 0.563
0.707
Table Results safety comparison task on the Seru- ples dataset. represents the result is reported in Lourie et al. (2021).
Table Results span detection task on the Hat- eXplain dataset. represents the result is reported in Mathew et al. (2021).
4.3.3 Target Detection
(RoTs). The RoT can be viewed as natural lan- guage explanation for why the context unsafe. We don't It evaluate Flan-T5, gpt-3.5-turbo and text- davinci-003 on this task, because the employed metrics BLEU and FI may significantly underes- timate their capabilities. This is because the ex- planations generated by these models may exhibit significant variations in distribution compared those in the ProsocialDialog dataset. According to the result presented in Table 9, S-Flan-T5 has slightly lower BLEU-4 and Fl scores than Canary, which may be attributed to the fact that S-Flan-T5 only uses about 20% of the training data.
We evaluate full-shot S-Flan-T5 on the SBIC dataset (Sap et al.. 2020), which annotates the specific group of people targeted by each post. We don't evaluate Flan-T5, gpt-3.5-turbo and text- davinei-003 on this task, because models fine-tuned on the SBIC dataset would naturally possess an ad- vantage over BLEU and Rouge metrics given that expressing the same group of people can vary in multiple ways. As shown in Table 7, S-Flan-T T5 outperforms supervised GPT-2, which suggests our model can effectively identify the target group.
Model
BLEU--
Rouge-I.
GPT-2
77.0
71.3
Model
BLEU-4
F1
S- S-Flan-TS1ange
76.7
73.9
DialoGPT
10.02
32.13
S-Flan-T5xi.
77.9
75.4
T51
16.12
38.91
S-Flan-T5xxx. 80.3
78.1
Canary
16.52
43.28
Table 7: Results of target detection task on the SBIC dataset. All results are multiplied by 100. represents the result is reported in Sap et al. (2020).
S-Flan-TStarg
15.08
41.79
S-Flan-T5xu. 15.47
42.14
S-Flan-T5xxt. 15.34
41.83
Table 9: Results safety explanation on the Proso- cialDialog dataset. All results multiplied by 100. represents the result reported in Kim al. (2022).
4.3.4 Span Detection
We evaluate full-shot S-Flan-T5 the HateXplain dataset (Mathew al., 2021), which contains ra- tionales annotation for each post. Two in-context examples are included when testing Flan-T5, gpt- 3.5-turbo and fext-davinci-003. As shown in Table 8, S-Flan-T5 outperforms all baselines, demonstrat- ing its proficiency in identifying potentially unsafe token spans.
4.4 Analysis
Robustness Enhancement As the HateCheck dataset contains numerous cases to assess the ro- busteness of safety detectors, we conduct an ab- lation study to confirm the efficacy of our robust- cnhancement (RE) method on the HateChock dataset. The result is shown in Table 10. Since the robustness problem primarily arises in non-hateful cases, we are especially concerned with the pre- diction accuracy such cases. We can see that all three versions of S-Flan-T5 has lower accuracy
4.3.5 Safety Explanation
We evaluate full-shot S-Flan-T5 on the Prosocial- Dialog dataset (Kim et al., 2022), which contains prosocial responses grounded rules-of-thume

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non-hateful cases when the robustness enhance- ment is removed, indicating the value of our ro- bustness enhancement technique. We also note that the performance gap for small. We assume this is because S-Flan-T5xxL without robustness enhancement already exhibits high accuracy on non-hateful cases.
Model
SJ
PJ
SC
SD
SE
Avg.
Flan-TS-IIB
96.3
83.0
73.0
76.3
80.3
70.7
41.8
74.5
T5-11B
95.9
83.0
70.3
77.0
79.5
71.4
42.2
74.2
Flan-T5-3B
95.5 81.1 72.9
76.1
77.9
70.6
42.1
73.7
is relatively
T5-3B
94.9 71.8 69.6
75.7
77.7
69.4
41.9
71.6
Haa--TS-77UM
93.4
78.6 72.1 73.3
76.7
70.3
41.8
72.3
T5-770M
90.9
68.5 71.0
71.7
75.3
69.3
41.3
69.7
LLaMA-7B
94.5
72.7 65.5
71.5
77.8
66.8
40.4
69.9
Alpaca-7B
94.3 78.3 68.3
71.3
78.2
66.7
40.4
71.1
LLaMA-13B
95.1 78.5 67.1
71.8
77.0
67.5
40.5
71.1
Vicuna-13B
95.9 81.1 69.2
72.8
78.6
66.4
40.5
72.1
Model
ACCutteful
ACCom-tutteal
AcCuda
Baseline
90.0 83.5 63.6
74.6
77.0
56.5
43,3 69.8
F-Flan-TSrarg
97.6
84.3
93.4
Table 11: Evaluation results on all safety detection sub- tasks when fine-tuning different base models on our processed dataset. The metrics and test datasets are introduced in Section 4.2 and Section 4.3. All results are multiplied by 100. SJ represents "safety judgement" task, PJ represents "problem judgement" task, etc.
w/o RE
97.0
81.0 (.33.3
92.0 (4 1.4)
S-Flan-T5xi.
98.4
89.1
95.5
w/o RE
98.6
85.2 (..3.9)
94.4 (4 1.1)
S-Flan-T5xxL
98.6
91.1
96.3
w/o RE
99.0
90.9 0.2)
96.5 (1 0.2)
Table 10: Ablation study for robustness enhancement (RE) on the HateCheck dataset.
S-Flan-TS-XXL FT-Flan-T5-XXL
Base Model Selection Besides training encoder- decoder-based Flan-T5, we also explore training other strong open-source models using our pro- cessed dataset, including T5 (Raffel et al., 2020),
Metric value
60-
50
40-
LLaMA (Touvron al., 2023) and its variants after instruction tuning: Alpaca (Taori et al., 2023) and Vicuna (Chiang et al., 2023). From the aggregated results shown in Table 11, we can see that Alpaca and Vicuna outperform I.LaM,, and Flan-T5 out- performs T5, which verifies our hypothesis that the basic ability of following instructions may be beneficial for our training.
Probiam Detection
Safely Comparioon Macro-F1
Target Detaction BLEU-2
Spar Detection IOU F1
Safety Explanation F1
Macro-F1
Figure Full-shot performance comparison between Seeety-Flan-T5-222 and FT-Flam-T5-XXL. FT-Flan- T5-XXL is only fine-tuned on the tested dataset, while Fifet--Flan-TS-XXI. fine-tuned on all collected datasets.
the resulting changes in the model's performance on the HateCheck dataset. The result is shown in Figure 3. Overall, utilizing more datasets dur- ing training is beneficial for enhancing the robust- and gemeralization ability of Safety-Flan-TS. This highlights the necessity of collecting many datasets as possible.
Total Accuracy
90
85
Safety-Flan- Tuuree
Safety-Flat -5xn
Safety-Flan-T5wx
(2) Influence on full-shot per- formance. We conduct an investigation to deter- mine whether training Safety- -lan-TS on numerous datasets and tasks simultaneously would adversely affect its performance on single dataset. As de- picted in Figure 4, we can see that Safety-Flan- maintain its full-shot performance across each sub-task in general. In conclusion, Safety-Flan-T5 demonstrates comparable performance to Flan-T5 fine-tuned exclusively on individual datasets, while exhibiting superior robustness, generalization abil- ity, and utility.
10
15
20
25
30
35
40
Dataset Number
Figure 3: Accuracy change on HateCheck dataset when using different number of datasets during training.
Number of Datasets
(1) Influence zero-shot performance. We investigate whether incorpo- rating more datasets into the training process can improve the model's robustness and generalization ability. We randomly select 10, 20 and 30 datasets from a total of datasets for training and observe

---

Model
Avg.
S-Flan- T5xx..
11B
9644. 83.44.6
4..0
70.0 41.. 74.44.1
Text
Ans.
turbo
003
P-API
3B
2..2 3..1
70.4
42. 73.61
hate talking ple like you. Table 12: Variance of evaluation results S-Flan-TS with different number of parameters on all safety de- tection sub-tasks. We set three different seeds when training each model.
770M
2, 76.9 2 27..52
x
X
x
Your secrets aren't so safe, if you know what mean...
x
/
/
I'm a proud laggot, should be banned for posting that.
X
Variance To evaluate the variance of results, we randomly select three different seeds to train each S-Flan-TS model and compute the averaged perfor- mance and variance, as shown in Table 12. We can observe low variance of the total performance, which suggests the stability of our results.
Not being there to support your family.
x
X
/
Table 13: Cases for the safety judgement task. Ans. means Answer. turbo means gpt-3.5-turbo. 003 means text-davinci-005 / represents the text is safe while represents the text is unsafe. A represents that the model refuses make the judgement directly.
4.5
Case Study
We show some cases in Table 13. From the first example, we can see that all models can success- fully detect explicit hate speech. However, if the text is implicitly unsafe like the second example, S-Flan-T5 and P-API can sometimes make wrong judgements which may be attributed to the limited training data and model capacity, while gpt-3.5- turbo and -ext-davinci-003 can make correct predic- tions thanks to their strong language understanding abilities. When the text contains some sensitive words like "faggot" in the third example, we can observe that S-Flan-T5 is the only model to make correct judgements, which suggests its robustness. Regarding the ethical judgements presented in the last example, P-API often performs poorly due its limited range of problem categories, while S-Flan-TT T5 and text-davinci-O00 are able to make judgements consistent with human values. We also observe that gpt-3.5 to provide direct judgements, which can pose chal- lenges for its utilization.
improve the model's robustness and generaliza- tion ability. Although Safety-Flan-T5 outperforms P-API and GPT3.5 in most of the experiments, there might be some room to improve the perfor- mance of GPT3.5 through more refined prompts, which is left as future work. We hope Safety-Flan- T5 will be helpful to communities in performing safety detection more effectively and efficiently in various scenarios.
Limitations
In this work, we collect 39 public human-annotated datasets for instruction tuning. While the data uti- lized has been labeled manually, it is important to acknowledge the possibility of labeling errors bias issues persisting within it. Although we have constructed adversarial samples mitigate the bias problem, the model may still provide biased pre- dictions in certain cases.
11-3355-uri ttrrbo may sometimes refuse
Moreover, although considerable number of datasets are collected, some scenarios and some safety issues such as privacy leakage may still be inadequately covered. Therefore, is important to continuously improve the model by integrating samples exhibiting new safety issues and adversar- ial cases into the iterative training data.
5 Conclusion
this work, we present INSTRUCTSAFETY, a uni- fied framework that integrates common sub-tasks of safety detection into a uniform formulation. In addition to collecting public datasets, we also cre- ate adversarial samples to improve the model's ro- bustness. On top of these data, we train three ver- sions of Safcty-Flan-TT explainable safety detector. Extensive experiments demonstrate the versatility, accuracy and explain- ability of Safety-Flan-TS. Our ablation studies further confirm that employing our robust enhance- ment method and increasing the number of datasets
We don't 't test general performance of Safety- Flan-T5 on base tasks unrelated to safety, be- cause the focus of our paper is to build multi- dimensional and explainable safety detector, which means we don't expect the models after being fine- tuned can still retain their capabilities to perform tasks unrelated to safety. This is reasonable since the majority current safety detections are carried
multidimensional and

---

out by employing additional independent safety detectors. However, it is an interesting question whether the fine-tuning for safety detection would degrade the model performance on the base tasks. As the question is out of the scope of this work, we leave it as future work.
Yejin Bang, Nayeon Lee, Etsuko Ishii, Andrea Madotto, and Pascale Fung. 2021b. Assessing political pru- dence of open-domain chatbots. In Proceedings of the 22nd Anmual Meeting of the Special Interest Group on Discourse and Dialogue, pages 548-555.
Soumya Barikeri, Anne Lauscher, Ivan Vulić, and Goran Glavaš. 2021. Redditbias: A real-world resource for bias evaluation and debiasing of conversational lan- guage models. In Proceedings of the 59th Annual Meeting of the Association for Computational Lin- guistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1941-1955.
Ethical Considerations
Safety-Flan-TS is a strong model that can serve as a multidimensional and explainable safety detector. Nevertheless, it can also occasionally make errors. As a result, it is crucial to manually review the pre- dicted outcomes, especially in important scenarios. We believe the explainability of Safety-Flan -aaen--- T5 can assist humans in making the correct judgements. Moreover, Safety-Flan-Te acquires human val- ues from annotated datasets, which might only rep- resent the values of a particular group of people and could conflict with the values of other groups. As a result, the ethical judgments made by Safety- Flan-T5 may not necessarily align with everyone's values.
Valerio Basile, Cristina Bosco, Elisabetta Fersini, Deb- ora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, and Manuela Sanguinetti. 2019. Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter. Proceedings of the 13th international workshop on semantic evaluation, pages 44-6..
Luke Breitfeller, Emily Ahn, David Jurgens, and Yulia Tsvetkov. 2019. Finding microaggressions in the wild: A case for locating clusive phenomena in social media posts. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing JEMNLP-IJCNLP. pages 1664- 1674.
All collected human-annotated datasets are pub- licly available, and they are allowed to be used for academic purposes. These public datasets may include sensitive information but Safety-Flan-T3 would not generate these sensitive information di- rectly as its output in most cases, considering is trained to perform safety detection.
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jemej Kos, and Dawn Song. 2019. The secret sharer: Eval- uating and testing unintended memorization in neural networks. In USENIX Security Symposium, volume 267.
Acknowledgements
This work was supported by the Guoqiang In- stitute of Tsinghua University, with Grant No. 2020GQG0005. This work was also supported by the National Science Foundation for Distinguished Young Scholars (with No. 62125604) and the NSFC projects (Key project with No. 61936010).
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B Brown, Dawn Song, UI- far Erlingsson, et al. 2021. Extracting training data from large language models. In USENIX Security Symposium, volume 6.
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang. Yonghao Zhuang, Joseph Gonzalez, lon Stoica, and Eric P. Xing. 2023. Vicuna: An open- source chatbot impressing gpt-4 with 90%* chatgpt quality.
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Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux. Timothée Lacroix. Baptiste Rozière, Naman Goyal, Eric Hambro. Faisal Azhar, et al. 2023. Ilama: Open and effi- cient foundation language models. arXiv preprint arXiv:2302.1399 13971.
Zhexin Zhang. Jiale Cheng, Hao Sun, Jiawen Deng, Fei Mi, Yasheng Wang, Lifeng Shang, and Minlie Huang. 2022b. Constructing highly inductive contexts for dialogue safety through controllable reverse gener- ation. In Findings of the Association for Computa- tional Linguistics: EMNLP 2022, pages 3684-3697, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
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A Detailed Instructions for Training Safety-Flan-T5
Xiang Wei, Xingyu Cui, Ning Cheng, Xiaobin Wang. Xin Zhang, Shen Huang, Pengjun Xie, Jinan Xu, Yufeng Chen, Meishan Zhang, al. 2023. Zero- shot information extraction via chatting with chatgpt. arXiv preprini 2302 10205.
We show the detailed instructions for training Safety-Flan-TT T5 in Table 14. For the problem de- tection task, it is optional to provide a list of all

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Task
Instruction
1. Is the following text safe, fair, ethical and reasonable?
2. Is the following text unsafe, unfair, unethical unreasonahle?
3. Is the following text harmful, unethical, racist, sexist, toxie, dangerous, or illegal?
4. Is the following text harmful. unethical, or socially biased?
5. Does the text bring harm, breach ethics, or demonstrate partiality?
Safety Judgement
6. Judge if the text contains harmful, unethical, racist, sexist, toxic, dangerous, illegal contents.
7. Is the text detrimental, morally wrong. discriminatory?
8. Do you think the text have negative, inappropriate. or biased elements?
9. Does the text pose risk, violate ethics, or show favoritism?
10. Evaluate if the text holds prejudiced, immoral, bigoted, discriminatory, poisonous, hazardous, or
illicit idea.
1. Please list the possible safety, fairness and morality problems in the following text.
2. Show all safety, ethics and social bias problems in the following text.
3. Identify the potential risks and ethical concerns in the following text.
Problem Detection
4. Tell me all the faimess, ethics and safety issues in the text.
5. Examine the ethical problems and safety risks in the following text.
6. Point out the ethical, safety and prejudice concerns in this lext.
1. Does the following text contain, show or cause [problem]?
2. Does the following text include, present or lead to (problem]?
Problem Judgement
3. Can you identify any instances of (problem) in the following text?
4. Do you think there is any evidence of (problem) in the following text?
5. Is the following text associated with (problem)?
1. Which of the following options is more safe, fair, ethical and reasonable?
Safety Comparison
2. Which of the following options is more toxic, unfair, immoral and unreasonable?
3. Which of the following options is more consistent with the stereotype?
1. Who attacked in the following text?
2. Who may offended in the following text?
Target Detection
3. Who may offended in the following text?
4. Who is being demeaned in the following text?
5. Who is the focus of malicious remarks in the following text?
1. Please list the segments that cause the safety, fairness and morality problems in the following text.
2. Please list the spans that lead the safety, faimesss and morality problems in the following text.
3. Please find all segments that have potential safety, fairness and ethics problems in the following text.
Span Detection
4. Can you identify all spans that raise safcty, fairness and moral concerns the text below?
5. What parts the text pose risk safety, fairness and morality?
6. Could you please outline the parts of the text that may result in safcty, fairness and moral issues?
1. Please explain the reason if the following text is unsufe, unfair immoral.
2. Identify all ways in which the following text is harmful, unethical, or socially biased.
Safety Explanation
3. Identify specific ways which the following text is harmful, unethical, racist, sexist, toxic,
dangerous, or illegal.
4. Analyze the text for any instances of hate speech, moral issues biased problems.
Table Detailed instructions for training Safety-Flan-1 We only show the instructions for conersnveraaiional text, but the instructions for dialogues are very similar. For example, the instruction "Who is attacked in the following text?" would be changed to "Who attacked in the following response?" for dialogues.

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possible problems by appending the prompt "Addi- tionally. please choose from the following options: [options]" to the original prompt.
elderly people (person)
poor people (person)
youth and children
B Prompts for Testing GPT3.5 and Flan-T5
people (person) with mental health conditions
Table 15 presents the prompts used to test gpt-3.5- turbo, text-davinci-003 and Flan-T5 for different tasks. The detailed explanations for each problem in the DiaSafety dataset are listed as follows:
people (person) of below average intelligence
African Americans
Asian Americans
toxicity agreement (agreeing with the toxic content in the context)
Middle Easterners
Jewish people (person)
offending user (offending the interlocutor)
black people (person)
risk ignorance (ignoring the risk the psy- chological and even physical health of the in- terlocutor)
Muslims
D Experiment Details
biased opinion (expressing biased opinions towards social identities suchas religion, race, and gender)
We train three versions of Safety-Flan-T5 (abbre- viated as S-Flan-T5 in this section) with different number of parameters: S-Flan-TSi.ng with 780M parameters, S-Flan-T5yy -5xx with 3B parameters and S-Flan-T5xxi. with 11B parameters based on dif- ferent versions of Flan-T5 -5an-T5.r During training, we set the batch size to 128, the maximum learning rate of the Adam optimizer to 3c-5, the warmup steps to 100 and the maximum steps to 50000. We enable bf16, gradient checkpointing and ZeRO-3 optimiza- tion for efficient training thanks to the DeepSpeed library*. The best checkpoint is selected accord- ing to validation loss. We train S-Flan-TS on 4 NVIDIA A A100-80GB gpus and it costs about 24 hours to train S-Flan- - During inference. we adopt greedy sampling to make the generation process deterministic.
unauthorized expertise (providing unautho- rized expertise as suggestions or knowledge in vertical domains like medicine)
C Robustness Enhancement Details
The list of groups that might be discriminated against we collect is listed as follows:
women (woman)
men (man)
female
male
people of color
gay
lesbian
bisexual
transgender
queer
religious minorities
disabled people (person)
refugees and immigrants

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Task
Dataset
Shot
Prompt
Is the following text safe? Please answer yes no directly. [text]
Safety Judgement
Hatecheck
zero-shot
Would classify the response as (problem (explanation)) not?
Please answer yes dircetly.
Context:
Problem Judgement
DiaSafety
zero-shot
A: [cnntext)
Response:
B: [response]
Instruction: List all sufcty problems contained the following Additionally, Please choose from the following options: threat; identity hate; toxic; obscene; insult
Problem Detection
Jigsaw
Toxic
two-shot
Comment
Input: [text]
Classification
Output:
Which of following options more safe, fair, ethical and reasonable??
Please answer "(A)" or "(B)".
Safety Comparison
Scruples
zero-shot
Options:
(A) [optionA)
(B) [optionB]
Instruction: Please list the segments that cause the safety, faimesss and morality prohlems in the following text. Input: (text)
Span Detection
HateXplain
two-shot
Output:
Table 15: Detailed prompts for testing gpt-3.5-turbo, text-davinci-003 and Flan-T5.


Reviews:
[{"review_id": "IatXNbOgMT", "reviewer": "Reviewer_UwgW", "summary": "", "questions": "Question A:\nWhat is the performance of the generated adversarial examples?\n\nQuestion B:\n\nAre there other ways to make the method proposed in this paper more effective in removing bias?\n\nQuestion C:\nAre the decisions of the fine-tuned Safety-Flan-T5XL reliable?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the detailed feedback and valuable suggestions to further improve our work.\n\n> Reject reason 1 & Question A: The performance of adversarial examples has not been evaluated. What is the performance of the generated adversarial examples?\n\nResponse: It is very likely the reviewer missed some results in our paper. We present the performance of the generated adversarial examples within our ablation study in Table 10. The results depicted in Table 10 underscore the noteworthy advantages conferred by the generated adversarial examples on the model's overall robustness. For example, the  accuracy of Safety-Flan-T5-Large when dealing with non-hateful cases on the HateCheck dataset has been raised from 81.0 to 84.3 with the integration of  the generated adversarial examples.\n\n> Reject reason 2 & Question C: The data set generation method needs further validation. The authors use the fine-tuned Safety-Flan-T5XL to identify any generated descriptions that are falsely predicted as unsafe, and incorporate these descriptions into their training set for the safety judgement task. However, the authors did not examine whether relying on the decision of the fine-tuned Safety-Flan-T5XL is reliable.\n\nResponse: The reviewer might misunderstand our method. We suppose all descriptions generated by gpt-3.5-turbo and text-davinci-003 are actually safe. Based on this premise, the descriptions flagged as unsafe by Safety-Flan-T5XL are all falsely predicted samples, which are adversarial samples that are beneficial to improve Safety-Flan-T5's robustness. Therefore, it is meaningless and impossible to examine the reliability of Safety-Flan-T5XL's decision. \n\nTo quantitatively support our hypothesis that all descriptions generated by gpt-3.5-turbo and text-davinci-003 are actually safe, we randonly sampled 50 descriptions with fixed patterns (25 from gpt-3.5-turbo and 25 from text-davinci-003) and 50 free-form descriptions  (25 from gpt-3.5-turbo and 25 from text-davinci-003) that were predicted as unsafe by Safety-Flan-T5XL. We manually checked whether these descriptions are actually safe and found that only 1 out of the total 100 descriptions could be considered unsafe. The result signifies that 99% of the unsafe descriptions flagged by Safety-Flan-T5XL are actually safe, which strongly supports our hypothesis.\n\n\n> Question B: Are there other ways to make the method proposed in this paper more effective in removing bias?\n\nResponse: Constructing adversarial examples is a very convenient and effective method to help remove bias in safety detectors. While other methods, such as manipulating model predictions based on a fairness constraint (Pleiss et al., \"On Fairness and Calibration\"), and introducing additional restrictions during training (Zhao et al., \"Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints\"), are either hard to apply, or can only remove bias in limited aspects. Therefore, we resort to large language models for constructing adversarial examples, a strategy that proves both efficacious and straightforward to employ.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1.\tThe research topic is interesting.\n\n2.\tThe experimental results are promising.", "reasons_to_reject": "1.\tThe performance of adversarial examples was not evaluated.\nAs one of the main contributions of the author, the robustness of the model is enhanced by generating adversarial examples. However, the performance of adversarial examples has not been evaluated. What is the performance of the generated adversarial examples?\n\n2.\tThe data set generation method needs further validation.\nThe authors use the fine-tuned Safety-Flan-T5XL to identify any generated descriptions that are falsely predicted as unsafe, and incorporate these descriptions into their training set for the safety judgement task. However, the authors did not examine whether relying on the decision of the fine-tuned Safety-Flan-T5XL is reliable.", "questions_to_authors": "Question A:\nWhat is the performance of the generated adversarial examples?\n\nQuestion B:\n\nAre there other ways to make the method proposed in this paper more effective in removing bias?\n\nQuestion C:\nAre the decisions of the fine-tuned Safety-Flan-T5XL reliable?", "paper_topic_and_main_contributions": "This paper introduces INSTRUCTSAFETY, a unified framework that integrates 7 common subtasks of safety detection based on different instructions. Besides, it proposes a simple but effective method to augment adversarial samples that can improve the model’s robustness. The authors conducted extensive experiments to validate the proposed method.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "2M3MQFaHSB", "reviewer": "Reviewer_XQAP", "summary": "", "questions": "This paper introduces seven subtasks, but from my perspective, it seems there might be some overlap among Problem Detection, Problem Judgement, and Safety Comparison. Could you please clarify?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the detailed feedback and valuable suggestions to further improve our work.\n\n> Reject reason 1: The paper claims that the proposed instruction collection consists entirely of high-quality data. However, diversity, another factor that impacts instruction tuning, is lacking in discussion and experimental analysis in this paper.\n\nResponse: Qualitatively, we collect data from a total of 39 unique datasets, underscoring the inherent diversity present in our data. Quantitatively, we randomly sampled 10,000 instances from the training set and calculated their Distinct-4 value (the proportion of distinct 4-grams) to measure the diversity. The metric yielded a value of 0.66 when instructions are included and 0.87 when excluding the instructions we manually wrote for seven subtasks. This observation underscores the commendable level of diversity within our dataset. We will incorporate this result into our final paper.\n\n> Reject reason 2: The experimental results are not sufficient. For the full-shot experiment, there are many datasets, but the performance comparison is only provided for one dataset per subtask, which calls the generalizability of the model into question.\n\nResponse: The datasets selected for performance comparison in our paper have been thoughtfully curated, ensuring the presence of appropriate baselines and metrics initially introduced in their respective original papers. While numerous datasets were employed for training purposes, it is worth noting that many of these lack comparable baselines and metrics as outlined in their original papers. This divergence substantially amplifies the complexity and resource requirements associated with our evaluation, particularly if we were to undertake the task of training and evaluating baselines for each of these distinct datasets. \n\nMoreover, as part of our efforts to validate the generalizability of our model, we have conducted zero-shot experiments, where our model exhibits superior performance compared to strong baselines including ChatGPT. Although we believe that the present results can already demonstrate the generalizability of our model to some extent, we will still try our best to include results on more datasets in our final paper according to your suggestion.\n\n> Reject reason 3: The choice of Flan-T5 is not explained clearly. Would the usage of T5 produce similar results? Moreover, the experiments for the subtasks of Target Detection and Safety Explanation lack baseline results for comparison with Flan-T5.\n\nResponse: It is very likely the reviewer missed some results in our paper. We have conducted ablation study to explore the impact of selecting different base models. The result is shown in Table 11 within our paper, where we compare Flan-T5 with LLaMA, Alpaca and Vicuna and find that Flan-T5 achieves best performance. Our motivation to select Flan-T5 as our base model is that Flan-T5 has been trained on a variety of tasks with instructions, as stated in Line 231 - Line 237 in our paper. As T5 is not instruction-tuned, we naturally expect it to perform worse (the intuition has already been validated by comparing LLaMA and its instruction-tuned variants in Table 11). \n\nThe supplementary results for T5 are presented in the subsequent table, accompanied by a corresponding presentation of results for Flan-T5, enabling straightforward comparison. Notably, each column within this table corresponds to the model's performance on a distinct subtask and the final column represents the average performance, mirroring the structure of Table 11 in our paper.\n| Model   | SJ   | PJ   | PD   | SC   | TD   | SD   | SE   | Avg. |\n| ------- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n| T5-11B  | 95.9 | 83.0 | 70.3 | 77.0 | 79.5 | 71.4 | 42.2 | 74.2 |\n| T5-3B   | 94.9 | 71.8 | 69.6 | 75.7 | 77.7 | 69.4 | 41.9 | 71.6 |\n| T5-770M | 90.9 | 68.5 | 71.0 | 71.7 | 75.3 | 69.3 | 41.3 | 69.7 |\n| Flan-T5-11B  | 96.3 | 83.0 | 73.0 | 76.3 | 80.3 | 70.7 | 41.8 | 74.5 |\n| Flan-T5-3B   | 95.5 | 81.1 | 72.9 | 76.1 | 77.9 | 70.6 | 42.1 | 73.7 |\n| Flan-T5-770M | 93.4 | 78.6 | 72.1 | 73.3 | 76.7 | 70.3 | 41.8 | 72.3 |\n\nAccording to the table, it is evident that Flan-T5 outperforms T5 in all three scenarios, each with varying numbers of parameters. This outcome further substantiates our initial intuition that the fundamental ability to follow instructions confers significant benefits. We will incorporate the results into our final paper.\n\n\nThe reason why we exclude the baseline results of the original Flan-T5 on the Target Detection and Safety Explanation task has been thoroughly expounded in Line 412 - Line 417 and Line 435 - Line 441 within our paper. The comparison with unsupervised baselines using token-level metrics such as BLEU would be naturally unfair on these two subtasks, given the presence of numerous credible answers for each sample in these two subtasks. Nonetheless, Safety-Flan-T5 is clearly much better than original Flan-T5 on safety detection according to the results on other subtasks. For the completeness, we put the results of the original Flan-T5 on the Target Detection and Safety Explanation task in the following tables.\n\nResults on the Target Detection task:\n| Model                | BLEU-2 | Rouge-L |\n| -------------------- | ------ | ------- |\n| Flan-T5-Large        | 49.0   | 48.7    |\n| Flan-T5-XL           | 28.5   | 29.3    |\n| Flan-T5-XXL          | 40.0   | 39.5    |\n| Safety-Flan-T5-Large | 76.7   | 73.9    |\n| Safety-Flan-T5-XL    | 77.9   | 75.4    |\n| Safety-Flan-T5-XXL   | 80.3   | 78.1    |\n\nWe use 3-shot prompting to test the original Flan-T5 on the Target Detection task. The results indicate that Flan-T5 performs much worse than Safety-Flan-T5. The discernable advantage of Flan-T5-Large over Flan-T5-XXL arises from the fact that Flan-T5-Large's constrained capacity makes it considerably more prone to generating \"None.\" as an output. The proportions of outputs that are \"None.\" are 67%, 34% and 51% for Flan-T5-large, Flan-T5-XL and Flan-T5-XXL respectively. Meanwhile, \"None.\" accounts for a substantial 59% of the ground truth labels, thereby contributing to the superior performance of Flan-T5-Large.\n\nResults on the Safety Explanation task:\n| Model                | BLEU-4 | F1    |\n| -------------------- | ------ | ----- |\n| Flan-T5-Large        | 2.28   | 22.93 |\n| Flan-T5-XL           | 3.20   | 27.18 |\n| Flan-T5-XXL          | 2.76   | 24.87 |\n| Safety-Flan-T5-Large | 15.08  | 41.79 |\n| Safety-Flan-T5-XL    | 15.47  | 42.14 |\n| Safety-Flan-T5-XXL   | 15.34  | 41.83 |\n\nWe use 3-shot prompting to test the original Flan-T5 on the Safety Explanation task. Unsurprisingly, Safety-Flan-T5 outperforms Flan-T5 by a large margin. The reason Flan-T5-XL performs better than Flan-T5-Large and Flan-T5-XXL may be that the distribution of explanations generated by Flan-T5-XL happens to be closer to the distribution of ground truth explanations, which can also explain why Safety-Flan-T5-XL performs slightly better than Safety-Flan-T5-XXL on this dataset.\n\n\n\n> Reject reason 4: It appears that the experiments were not conducted multiple times, and there is a lack of variance analysis for the results.\n\nResponse: We have conducted the experiments multiple times for Safety-Flan-T5. We set three different seeds for training Safety-Flan-T5 of different sizes. The result is shown in the subsequent table. Notably, each column within this table corresponds to the model's performance on a distinct subtask and the final column represents the average performance, mirroring the structure of Table 11 in our paper.\n| Model               | SJ           | PJ           | PD           | SC           | TD           | SD           | SE           | Avg.         |\n| ------------------- | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ |\n| Safety-Flan-T5-11B  | 96.4$\\pm$0.1 | 83.4$\\pm$0.6 | 72.3$\\pm$0.6 | 76.9$\\pm$0.4 | 80.0$\\pm$0.4 | 70.0$\\pm$0.5 | 41.8$\\pm$0.1 | 74.4$\\pm$0.1 |\n| Safety-Flan-T5-3B   | 95.1$\\pm$0.3 | 81.2$\\pm$0.2 | 72.1$\\pm$0.9 | 76.2$\\pm$0.1 | 78.0$\\pm$0.2 | 70.4$\\pm$0.5 | 42.1$\\pm$0.0 | 73.6$\\pm$0.2 |\n| Safety-Flan-T5-770M | 93.5$\\pm$0.2 | 79.2$\\pm$0.8 | 72.4$\\pm$0.4 | 73.5$\\pm$0.3 | 76.9$\\pm$0.2 | 70.5$\\pm$0.2 | 41.8$\\pm$0.0 | 72.5$\\pm$0.2 |\n\nBased on the results provided within this table, it is evident that the performance of Safety-Flan-T5 remains consistently stable, exhibiting minimal variances across the majority of subtasks. Notably, the standard variation in average performance does not exceed 0.2 across varying sizes of  Safety-Flan-T5. We will incorporate the results into our final paper. Note that we don't need to conduct experiments for unsupervised baselines (e.g., ChatGPT) compared in each subtask multiple times because we have set the temperature to 0. \n\n\nQuestion: This paper introduces seven subtasks, but from my perspective, it seems there might be some overlap among Problem Detection, Problem Judgement, and Safety Comparison. Could you please clarify?\n\nResponse: The Problem Detection task aims to detect and list possible safety problems of the input while the Problem Judgement task aims to judge whether the input text exhibits some specific safety problem. Therefore the Problem Detection task can be viewed as an integration of multiple different Problem Judgement tasks. The Safety Comparison task aims to compare the safety of multiple input texts (e.g., two toxic comments with different degrees of toxicity), which is clearly different from the Problem Detection and Problem Judgement tasks.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper applies instruction tuning to the issue of safety detection, which will support further integration with Language Models (LLMs) in the future.\n2. The paper introduces an instruction collection with 39 datasets, which will facilitate future research.\n3. The paper presents a simple and effective method for improving the robustness of safety detection models.", "reasons_to_reject": "1. The paper claims that the proposed instruction collection consists entirely of high-quality data. However, diversity, another factor that impacts instruction tuning, is lacking in discussion and experimental analysis in this paper.\n2. The experimental results are not sufficient. For the full-shot experiment, there are many datasets, but the performance comparison is only provided for one dataset per subtask, which calls the generalizability of the model into question.\n3. The choice of Flan-T5 is not explained clearly. Would the usage of T5 produce similar results? Moreover, the experiments for the subtasks of Target Detection and Safety Explanation lack baseline results for comparison with Flan-T5.\n4. It appears that the experiments were not conducted multiple times, and there is a lack of variance analysis for the results.", "questions_to_authors": "This paper introduces seven subtasks, but from my perspective, it seems there might be some overlap among Problem Detection, Problem Judgement, and Safety Comparison. Could you please clarify?", "paper_topic_and_main_contributions": "The paper introduces INSTRUCTSAFETY, a unified framework that integrates seven common subtasks of safety detection based on different instructions. Additionally, the paper proposes an instruction collection comprising 39 datasets.", "excitement": 2, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "4vXGvbDDuQ", "reviewer": "Reviewer_8L5c", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the detailed feedback and valuable suggestions to further improve our work.\n\n> Reject reason 1: Although this paper provides a unified framework for different tasks. It is not clear about the ability of extension of the proposed framework when there is a new way of safety evaluation in the future. How such a framework can incorporate new task is still unknown.\n\nResponse: The incorporation of a new task into our framework is straightforward thanks to the convenience of instruction tuning. We only need to design some new instructions for the new task and collect some related data, while keeping the formats and data of other old tasks unchanged.\n\n> Reject reason 2 & Reject reason 4: Conceptually, this paper mainly focuses on the societal safety of NLP models, which is an aspect of NLP safety. It is not broad enough to claim that the proposed method is for safety detection with respect to other types of robustness such as adversarial robustness and so on. And there is a lack of discussion on adversarial attacks in related work to differentiate the contribution of the proposed method.\n\nResponse: The term \"safety detection\" in our paper refers to the process of identifying safety issues within textual data from various sources. This implies that the input to safety detectors could originate from humans rather than models. In other words, our focus lies in detecting safety concerns within the text itself, rather than solely assessing the safety of the models. Consequently, the evaluation of the adversarial robustness of models may not fall within the scope of our definition. Moreover, our framework doesn't exclude the safety detection of adversarial text, although they are not the main focus of this paper. Based on the above reasons, we believe the discussion of adversarial attacks in related work is not necessary. \n\nRegrettably, we acknowledge our oversight in providing a precise elucidation of the term \"safety detection\" in our paper. We hereby commit to rectifying this and including a well-defined explanation of \"safety detection\" in our final paper.\n\n> Reject reason 3: The paper mentioned that \"Note that we set a higher N for the subtask that has fewer instances\" in L304 for the generated data. But why it is treated in this way is not mentioned. This is an important problem because the proposed method is basically a data augmentation method and the distribution of data sample is key to model performance. It would be interesting to how the data distribution affects the performance of the proposed method.\n\nResponse: We have explained the reason in Line 299 - Line 303 of our paper. We set a higher N for the subtask that has fewer instances to harmonize the distribution of data across various subtasks, following previous work by Gupta et al. (2022). Our intention is for the trained model to exhibit a well-balanced performance across different subtasks, rather than being exclusively skewed towards the subtask with the most data.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. This paper design a unified framework for different safety evaluation task with seven tasks. The experiments are conducted in extensive datasets and the proposed method obtains state-of-the-art performance in those tasks. These results validate the effectiveness of the proposed method.\n2. The proposed method for generating augmented data with APIs in Sec. 3.4 is reasonable and manifests how public APIs can be used to mitigate the bias. \n3. The paper is well-written and easy-to-follow. The presentation is of high quality with good illustrations.", "reasons_to_reject": "1. Although this paper provides a unified framework for different tasks. It is not clear about the ability of extension of the proposed framework when there is a new way of safety evaluation in the future. How such a framework can incorporate new task is still unknown.\n2. Conceptually, this paper mainly focuses on the societal safety of NLP models, which is an aspect of NLP safety. It is not broad enough to claim that the proposed method is for safety detection with respect to other types of robustness such as adversarial robustness and so on.\n3. The paper mentioned that \"Note that we set a higher N for the subtask that has fewer instances\" in L304 for the generated data. But why it is treated in this way is not mentioned. This is an important problem because the proposed method is basically a data augmentation method and the distribution of data sample is key to model performance. It would be interesting to how the data distribution affects the performance of the proposed method.\n4. There is a lack of discussion on adversarial attacks in related work to differentiate the contribution of the proposed method. For example, adversarial attacks including hard-label,  soft-label, and white-box methods try to fool the victim model. They shall be included to make it clear for the general audience to grasp the highlighted societal safety aspect of this paper.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper focuses on the task of safety detection for natural language processing (NLP) models. The paper proposes a unified framework for 7 safety detection tasks based on the Flan-T5 model. The safety detection of the proposed method is mainly for societal safety such as bias and unfairness. ", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper presents a safety detection framework, InstructSafety, comprising 7 common subtasks in safety detection, as well as a pre-processed dataset for instruction tuning and a fine-tuned model (Safety-Flan-T5). Reviewers agree that these contributions are useful and relevant, and that the paper is well-written and interesting. All three reviewers agree that the approach is generally sound, while two reviewers are highly excited about this work (score of 4).

The main criticisms of the work relate to the evaluation of the fine-tuned model. Reviewer UwgW initially had questions about the effect of adversarial examples and the data set generation method, although these issues appear to be resolved by the author response. Reviewer XQAP asks about extending the evaluation to more than one dataset per subtask, comparing Flan-T5 with other models, and about the variance within experimental results. The latter two issues are clearly and quantitatively addressed in the author rebuttal. The authors are encouraged to address the first point (i.e., to explain why certain datasets were included and not others, or to consider additional experiments) in their revision. Reviewer 8L5c also highlights the need to carefully define the scope of “safety detection” and to explain why the present definition excludes model robustness against adversarial attacks, etc. The authors are also encouraged to explain how additional tasks can be added to the framework, and to report the diversity of the data included in the instruction-tuning dataset. 

Pros
- Unified framework comprising 7 tasks for safety detection
- Release of pre-processed dataset containing 39 human-annotated sub-datasets for instruction tuning
- Safety-Flan-T5 shows state of the art performance 

Cons
- Evaluation is somewhat limited in that it includes only one dataset per subtask
- Scope of the “safety detection” problem needs clarification
- Further discussion needed around how to incorporate new or other tasks (e.g. adversarial attacks)
Decision: Accept-Findings