HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning
Authors: Yongjin Yang
Joonkee Kim
Yujin Kim
Namgyu Ho
James Thorne
Se-Young Yun
Venue: EMNLP 2023 Findings (2023)
Keywords: Hate Speech Detection
Large Language Models
TL;DR: Improving Hate Speech Detection System with rationales from Large Language Models

Abstract: With the proliferation of social media, accurate detection of hate speech has become critical to ensure safety online.
To combat nuanced forms of hate speech, it is important to identify and thoroughly explain hate speech to help users understand its harmful effects. Recent benchmarks have attempted to tackle this issue by training generative models on free-text annotations of implications in hateful text. However, we find significant reasoning gaps in the existing annotations schemes, which may hinder the supervision of detection models. In this paper, we introduce a hate speech detection framework, **HARE**, which harnesses the reasoning capabilities of large language models (LLMs) to fill these gaps in explanations of hate speech, thus enabling effective supervision of detection models. Experiments on SBIC and Implicit Hate benchmarks show that our method, using model-generated data, consistently outperforms baselines, using existing free-text human annotations. Analysis demonstrates that our method enhances the explanation quality of trained models and improves generalization to unseen datasets. Our code is available at https://github.com/joonkeekim/hare-hate-speech.git.

HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning
Warning: This paper contains examples of content that is offensive and may be upsetting.
Yongjin Yang" Joonkee Kim*
Yujin Kim* Se-young Yun
Namgyu Ho
James Thorne
KAIST AI [dyyjkd, joonkeekim, yujin399, itsnamgy, thorne, yunseyoungJekaist.ackk
Abstract
Given the post "How dark is your humor? it picks cottonn" this post speech?
Legend Iclss label target group
With the proliferation of social media, accu- rate detection of hate speech has become crit- combat nu-
Input
impliee meaning!
It thace speecht
ical to ensure safety online. anced forms of hate speech, it is important to identify and thoroughly explain hate speech help users understand its harmful effects. Recent benchmarks have attempted to tackle this issue by training generative models on free-text annotations of implications in hate- ful text. However, we find significant reasoning gaps the existing annotations schemes, which may hinder the supervision of detection mod- els. In this paper, we introduce a hate speech detection framework, HARE, which harnesses the reasoning capabilities of large language models (LLMs) to fill these gaps in explana- tions of hate speech, thus enabling effective supervision of detection models. Experiments on SBIC and Implicit Hate benchmarks show that our method, using model-generated data, consistently outperforms baselines, using ex- isting free-text human annotations. Analysis demonstrates that our method enhances the ex- planation quality of trained models and im- proves generalization to unseen datasets. Our code is available at https://g/ t foonkeekim/hare-hare hheee-eeeeteehhhh.eeeeeeeeeeeeee git.
This post targets Iblack Tolks,
implies that bblack folks are slaves.
!?
[a) Free-text text annotation by humans
By equating 'dark with "picking
cotton", tangets Black folks
2. African Americans historically associated with slavory ... implies folks slaves
Therefore it
speech
(b) Step-by-step reasoning by LiMs (Ours)
detection model
Figure 1: HARE uses large language models (LLMs) to generate hate speech explanations step-by-step. (a) Recent benchmarks on understanding hate speech provide free-text annotations on the implications of hate speech, but gaps in reasoning hinder the supervision of generative detection models. (b) We propose the use of LLMs to fill in the gaps and enable detection models to understand and explain hate speech.
(Liu et al., 2019). This can help mitigate distri- butional biases inherent in simple classification, allowing people understand and reason about the potential harms of hateful text (Sap et al., 2019b). Explanations can also improve the transparency of content moderation on social media (Gillespie. 2018).
1 Introduction
Recent works on hate speech understanding (Sap et al., 2019b; ElSherief et al., 2021: Huang et al., 2022) have considered training autoregressive lan- guage models to generate underlying explanations on hate speech. The models are trained on human- written free-text rationales such as implied state- ments and targeted groups. However, despite the use of novel benchmark datasets, i.e., SBIC (Sap et al., 2019b) and Implicit Hate (EIShericf et al., 2021), the trained models struggle generate detailed and comprehensive explanations. More- over, we observe that the provided rationales give marginal improvement to detection performance under joint training.
The increase in the use of online media has inten- sified the exposure to hate speech, prompting the need for effective detection systems (Schmidt and Wiegand, 2017; Fortuna and Nunes, 2018). While early works have been limited to the classification of explicit hate speech (Caselli et al., 2020; Mathew et al., 2021), recent works have drawn our attention implicit forms of hate speech which are more prevalent, yet subtle. (Jurgens et al., 2019).
To tackle these nuanced forms of hate speech, is important for systems to not only identify hate speech but also provide interpretable explanations
"qqul contribution corresponding authors

---

A potential cause of the limited supervision pro- vided by existing annotations on understanding and explaining hate speech may be the existence crit- ical gaps in reasoning. For example, as shown in Figure I, the implied statement of the post "How dark is my humour? It picks cotton" is annotated as "black folks are slaves", in SBIC. To understand this implication, one must understand that "dark" implies "black folks", and the phrase "picks cot- ton" relates to the historical background of African Americans. While this may be obvious to human annotators, language models are known to lack societal knowledge and commonsense reasoning skills to understand these nuances (Talmor et al.. 2019; Li et al., 2022; Choi et al., 2023). This leaves a significant gap between the training objectives of classification and generating annotated impli- cations, which may harm supervision (Wiegreffe et al., 2021b; Wang ct al., 2023a).
and Implicit Hate datasets, the task can also be for- mulated to sequentially output C, T, and then I, as [C; P). Hence, the model trained with human -written annotations is designed to generate C and then provide explanations using annotations T and I. However, due to a logical gap between the speech P and the annotations T and I, training model with these annotated rationales does not significantly enhance the model's ability to com- prehend hate speech.
Chain-of- of-thuught prompting (Wei et al., 2022) refers to a prompting method that generates chain of reasoning derive answers. Kojima et al. (2022) introduce a method of including the phrase "Let's think step by step" at the end of the prompt to generate reasoning in zero-shot setting.
2.2 Fr-HARE & Co-HARE
Drawing inspiration from the reasoning capabil- ities of large language models (LLMs) improved with chain-of-thouge (CoT) reasoning (Wei et al., 2022), we present our novel approach "Explain- able HAte Speech Detection with Step-by- Step REasoning (HARE)". We leverage LLM-generated free-text rationales using CoT prompts to fill in the gaps of reasoning in existing hate speech an- notations and enhance supervision of generative detection models. To create these rationales, we propose two approaches: (1) adopt CoT prompts to create comprehensive rationales that align with the given texts and (2) incorporate existing human annotations from benchmarks in the CoT prompts bridge the logical gap between the input text and human annotations. When tested on the challeng- ing SBIC and Implicit Hate datasets, our approach outperforms standard fine-tuning with given human annotations and provides enhanced explanations behind the detection results.
In this paper, we propose HARE, framework that fine-tunes using rationales generated by LLMs through CoT prompts. By leveraging these ratio- nales, the model is trained to discern whether a text is classified as hate speech or not. This ap- proach of utilizing the 4LM-generatee rationales not only improve the model performance as in pre- vious works (Ho ct al., 2022; Wang et al., 2023a) but also enables the model to generate more plausi- ble explanations. Subsequently, we introduce two variants that employ different methods for extract- ing rationales from LLMs.
Fr-HARE Our first strategy. Free-HARE (Fr-HARE), extracts rationales using LLM from the LLM without referencing the annotated rationales. In line with Kojima al. (2022), we employ zero-shot prompts prompt the LLM extract rationales, as follows:
Determine whether the following post is offensive, and explain why.
2 Method
Post: P
Answer: Let's explain step by step.
2.1
Preliminaries
The task of hate speech detection can be framed as generative task that inputs the text P and outputs prediction class C, formulated as p(C) P), indi- cating whether the speech is classified as "hate" or "not hate". Furthermore, by incorporating human- written rationales of the target groups T and im- plied statements I of hate speech from the SBIC
From the given prompt above, both the rationale R without any logical leap and the classification result C are generated. Then, we utilize the generated rationale R by training the model to sequentially generate C and R. represented as p([C; (P). Uti- lizing these rationales enhances the model's ability to comprehend the context of hate speech, thus lcading to explanations that are more intelligible to humans.
We refer to the "offensive" category in SBIC as "hate" class. In the experiment, we use the wording "offensive" as is.

---

CO-HARE We also propose second strat- egy, HoRditiomed-HARE (Co-HARE), which uti- lizes human-written rationales to formulate CoT prompts. Fr-HARE generates complete rationales entirely from scratch, disregarding human annota- tions included in the datasets. Although Fr-HARE offers the advantage of being universally applica- ble to any hate speech dataset, the LLM-generated rationales might lack consistency due wide varia- tion in reasoning sequences. To address this incon- sistency, Co-HARE integrates human-written ratio- nales about target groups T and implied statements I into the CoT prompt as follows:
3 Experiments
3.1 Experimental Setup
We utilize SBIC and Implicit Hate datasets for our fine-tuning experiments. Our models are trained to classify the offensiveness and hatefulness of posts, using SBIC and Implicit Hate, respectively. is noteworthy that in our Implicit Hate experi- ments, we combine both the explicit and implicit hate classes into a single "hate" category. We set up baselines with two families of models: C, a model trained exclusively for classification, and C+T+I, model trained using human- tionales. For Fr-HARE and Co-HARE, by using gpt-3. 5---3.---urb--0613 that is known for its rea- soning capabilities (Ouyang et al., 2022), we ex- tract four and cight different rationales per cach sample in SBIC and Implicit Hate, respectively, following the hyperparameter setting of Ho et al. (2022). Subsequently, we fine-tune the model, set- ting LLM- -eM-ggeeedeed rationales R and class C' as target sequence. For performance evaluation, we measure detection accuracy and compute the Fl score of classification, regarding "hate" as the pos- itive class. We make use of Flan-T5 (Wei et al.. 2021) with different model configurations: small, base and large. We also conduct experiments us- ing the large models of T5 (Raffel et al., 2020) and GPT-2 (Radford et al., 2019). A more detailed explanation of our experimental setup be found in Appendix B.
Determine whether the following post is offensive, and explain why. Provide brief, step-by by-sep explanation of how the post targets the specified group and how it leads to the implied statement provided.
Post: P
Target: 7'
Implied statement: I
Answer: Let's explain step by step.
Then, we train the model using extracted rationales, as in Fr-HARE. For samples labeled as "not hate" that do not include human-written rationales, we apply the prompt used in Fr-HARE. While CO-HARE requires human-written rationales, it generates ra- tionales that are more tailored to the specific re- quirements and features of the dataset, due to its guided nature. Therefore, the model trained with Co-HARE can provide explanations that align more closely with the forms of rationales that humans construct.
3.2 Results and Discussions
Do LLM-generated rationales improve detec- tion performance? Table presents the perfor- mance of hate speech detection according to dif- ferent methods on the SBIC and Implicit Hate datasets. Our strategies Fr-HARE and Co-HARE con- sistently exhibit superior performance over other baseline methods, regardless of the model size. This suggests that even though the baseline method is trained using human- written rationales, the more detailed and logically-sequence rationales of HARE can further aid the model in un- derstanding the input text and accurately classify- ing it as hate speech. Therefore, the results demon- strate that the quality of rationales has strong impact on classification. Furthermore, the perfor- mance of our method consistently improves the model size increases, contrast to baselines. This suggests that diverse reasoning becomes increas- ingly beneficial as scale grows. This notable im-
Details of HARE Once we have extracted the ra- tionales from the LLMs, we follow the approach of Kojima et al. (2022) to have the LLMs predict the class. Specifically, we employ two-stage ex- traction process. In the first stage, we extract both the class C and the rationale R from the LLMs us- ing our HARE method, represented as p([C; R|| P)), as previously outlined. In the second stage, we prompt the LLMs again, this time to predict the class C given the extracted rationales R and the post P. denoted as RCC P). During fine-tuning on hate speech datasets, if the predicted class C coincides with the true answer C, we concatenate C' with the extracted rationale R. If the predicted labels are incorrect, the models are solely trained to predict the class C. Furthermore, following the findings of Ho et al. (2022), we generate multiple distinct rationales to facilitate the learning process.
LLM-ggeeeated

---

Table 1: The performance of fine-tuning on SBIC and Implicit Hate dataset with various models and size.
Table 2:
Cross Evaluation results on HateXplain (Mathew et al., 2021) and DynaHate (Vidgen et al., 2020), We utilize Flan-T5-large fine-tuned on SBIC using each method.
SBIC
Implicit Hate
Model
Method
Ace
FI
Acc
F1
GPT-3.5-
ZS
80.06
81.75
73.58
65.66
HateXplain
DynaHate
Method
turbo-8613
ZS-CuT
73.48
79.07
73.98
67.19
C' 82.56
84.05
77.58
71.98
Acc
F1
Acc
F1
Flan-T5
C+T+I
82.99
84.05
77.63
72.39
C
64.40
74.18
64.35
67.41
small
Fr-HARE
84.18
85.18
79.33
73.29
C+T+I
68.84
74.52
64.72
67.41
Co-HARE
84.44
85.35
78.54
73.49
FA-
70.69
78.91
68.06
75.15
82.35
83.71
78.03
72.17
Co-HARE
71.62
78.52
69.98
75.01
Flan-T5 base
C+T+I
82.54
84.41
79.77
73.15
Fr-HARE
84,20
85.46
79.84
74.84
Co-HARE
84.65
85.76
80.38
75.69
10
C' 81.70
82,84
78.42
72.92
9
Flan-T5
C+T+I
83.48
83.70
80.14
73.10
large
Fr-HARE
85.21
86.16
80.49
74.62
I
Co-HARE
84,93
85,57
81.49
76.71
6-
5
83.03
83.53
78.79
72.50
C+T+1
Fr-HARE
Co-HARE
T5
C+T+1
84.23
85.21
79.61
73.80
(a) GPT-4 Single answer grading
large
Fr-HARE
85.27
86.32
81.61
75.59
Co-HARE
85,35
85,93
80.98
75.88
14%
C' 81.39
82,68
73.32
66.68
GPT-2
C+T+I 82.80
83.43
75.95
65.25
C+T41
Co-HARE
large
Fr-HARE
83,92
85.48
78.47
71.35
(b) GPT-4 Pairwise comparison
Co-HARE
84.64
85.67
80.07
71.58
The result of GPT-4 evaluation following Zheng et al. (2023). The and line represent the aver- age scores that range from I to 10 and 95% confidence interval, respectively. We utilize Flan-T5-1as -5lagge fine- tuned SBIC using each method.
Figure 2:
provement with HARE is achieved by using only 40$ for each method in approach, demonstrating that the ability to reason can be effectively trained with rationales from LLMs.
Additionally, while Fr-HARE and Co-HARE ex- hibit similar performance, Co-HARE has a slight edge in most cases. This is because Co-HARE is guided by hrmtnewritten annotations, which re- sults in better alignment with the setting of the datasets, we mentioned in Section 2.2. It is also noteworthy that all the fine-tuned models sur- pass both Zero-Shot (ZS) and Zero-Shot CoT (ZS- COT, Kojima et al. (2022)) classification perfor- mance of GPT-3. 2PTT3.5-turbbe 5-turb,, indicating that merely employing LLM with CoT prompts is not sufficient to tackle this task.
ability. Moreover, the comparable performance of Fr-HARE and Co-HARE suggests that both tech- niques of bridging logical gaps are effective in tack- ling hate speech detection.
Does HARE improve the quality of generated ex- planations? To evaluate the quality of the gen- erated explanations, we conduct automated eval- uation using GPT-4 (OpenAI, 2023) which has demonstrated its high aligmment with human eval- uation (Liu et al., 2023; Zheng et al., 2023). We randomly sample 50 "hate" instances from SBIC test where all methods correct and apply sin- gle answer grading (Zheng et al., 2023) expla- nations generated by models trained with C+T+I, Fr- HARE, and Co-HARE. Detailed explanation about GPT-4 evaluation can found on Appendix C.
Are HARE models more generalizable? To as- sess the ability our methods to generalize across different datasets, we evaluate the models fine- tuned on the SBIC datasets using each method on two distinct datasets, HateXplain (Mathew et al., 2021) and DynaHate (Vidgen al., 2020). Both datasets encompass forms of explicit and implicit hate. On both datasets, our methods Fr-HARE and Co-HARE both outperform baseline methods, indi- cating that our methods enhance the generalizabil- ity of the models by improving their reasoning
The results depicted in Figure 2 (a) show that both Fr-HARE and Co-HARE are capable of gener- ating higher quality explanations, with Fr-HARE exhibiting slightly superior performance, suggest- ing that the ficxibility of Fr-HARE leads to more qualified explanation.

---

Poot
like un wonea my cigur 7 yoars old and comning Goon in budaa sack.
Why sann while people own pets? Becsuse they can't people anyrarre.
Label
(Ai Offensive
Label
(A) Offensive
Ansotation
gick not weelly of equal life girkl propanty girls usal fir sex invilines horm to vierms
Assotation
back people infecix black peogle should be slaves
The nakes generilaation
white people thei owzership
C-T-4
The newerr (00 Not offensive
that white people hisuncally owned
The asswer (A) Octassive T.The post compeece wassen to cigan, whicch ofgertiyynng dehurmmoing age group which
davery implice that people
applasatton uwiing people, This post can consizsee ofletsive bocuuee perpetantcs barudl stesootppe about white people and miskes light at dark and patstgl hissory of slavery It redices the complex iasse slaveyy to simpistic and otteneve joke. While humor can be sabsoctiee this poul crosss the line uaking auppingrist and insersiivee compations..
Fr-HART
inspreopiatc offereeve.
LLM cational
The asswer 1A Ofiassive The past compeece wozenn to eigas, whichh obsectiiing and dchumenizing, (...) The post specitically mecaiioss that the eigans should be years old. which hights trappeopeinte and offeneive il digammoss child exploitation and siane (..)) The post sho mestions duat the cipaes dhodil be "cusiing fioms Cube i builap sack." which is clea jcteecace to ses uallickaig scandal list occurred in the Liittd States in pasi. (..)
Cc-HARE
Figure 4: A sample of LLM rationale generated by
GPT-3. 5- using Fr-HARE from SBIC train set.
lot, but don't say mudh"" Anee Frank locly dic's been anythin for almost centery
Poot
proven
Label
(A) Offessive
sence of overtly derogatory words might lead some models to classify the statement as non-offensive. However, our approach appropriately identifies the underlying hateful context.
Ansotation
jewisl people were rounded and killed in the lolocaust: / jewas people were victins of genocide.
C-T41
The answer is: (BV Nox offensive
The asswerr (A) Offassive The post viatts quite Aiss died duitng the Holocasal
Frunk, Jewish
quote is
statement about
of speaking much 3. I..3 invillzes Hoolccaus makes light suttering and death Are Frani
In the second sample, the post contains hate speech targeting Jewish victims of the Holocaust by referencing Anne Frank. Our model accurately rec- ognizes the historical background of Anne Frank as a Holocaust victim. While the baseline seems to overlook the historical significance associated with Anne Frank, our method correctly identifies her and assumes that the reference constitutes harass- ment against a Jewish victim, even though there is slight misunderstanding about the context of "lack of speaking" in Fr-HARE.
The asswer is (Ai Ofiaasive I. The past stats with quite Soo Atss Frunk, Jewsih idda
daay daras the Holocuuat 2 (....) This post uarget Hollocust vicaans and Jowin folks by making a joke shout Knne Frank's lock of speaking for almest centiry. It implles that ste has herr silmt silent about her expertince diring the Helaccest This usvialless the bann and suffocing duat Holocaust viciins cadured. (...)
C--HAR
Figure 3: Model outputs using baseline methods and our framework HARE. The samples are from SBIC test set. Note that the answers abbreviated by (...), and the full context is reported in Appendix D.
As Co-HARE designed to create rationales that align better with human- written rationales, wc also conduct pairwise comparison between Co-HARE and C+T+I to determine which method aligns better with human-written labels. Figure 2 (b) clearly shows that the rationales generated by the model trained using Co-HARE are aligned more to actual human-writte rationales than the ones gen- crated by the model trained directly with human- written rationales. This also highlights the value of Co-HARE, as it aligns better with natural intuition, indicating that both Fr-HARE and Co-HARE can be utilized for different purposes.
Is GPT-3.5 a qualified teacher? Since our framework is based on distillation of generated rationales from GPT-3.5 to smaller models, it is crucial to verify whether the teacher is qual- ified. Figure 4 displays rationales produced by GPT-3. 111-3.5-turbe 5utubo, which is employed to train the student model. This example illustrates that the LLM not only discerns the hateful nuances towards both white and black individuals, but also offers more detailed explanations compared to rationales written by humans. Notably, it accurately correlates the historical context, associating the word "slaves" with "pets". More analysis of rationales from can be found in Appendix D.2.
How does HARE detect and contextualize hate speech? Figure 3 presents model outputs using HARE and baseline method. A more detailed quali- tative study of randomly selected samples is avail- able in Appendix D. In the first sample, the post makes light of harming young girl using phrases like "like I like cigar" and "burlap sack". While model, Fr-HARE, does not explicitly connect the "burlap sack" to the idea of kidnapped girl, it does recognize the harmful implications towards the girl. Furthermore, Co-HARE understands the historical context behind the term "burlap sack".
Conclusion
In this paper, we present HARE framework to im- prove the ability of the language model to under- stand hate speech and provide clearer explanations for its decisions. We propose utilizing CoT rea- sonings extracted from LLMs in two variants overcome the logical gaps in human-annotatee ra- tionales. When fine-tuned on the SBIC and Implicit Hate datasets, our methods achieve superior detec- tion performance and better qualified explanations.
The presence of the positive term "like" and the ab-

---

Limitations
Evaluuaion (LTTP) grant funded by Korea govern- ment (MSIT) [No. 2021-0-00907, Development of Adaptive and Lightweight Edge-Collaborative Analysis Technology for Enabling Proactively Im- mediate Response and Rapid Learning, 90%] and [No. 2019-0-00075, Artificial Intelligence Gradu- ate School Program (KAIST), 10%].
While we assess the quality of explanations gen- erated by HARE using GPT-4, we do not conduct human evaluations, which are crucial for tasks re- quiring human-readable explanations. The primary reason for this omission is that the hate speech con- tent and its respective explanations could be exces- sively offensive for annotators and GPT-4 already aligns with the level of inter-human agreement. In addition, the "verbosity bias", characterized by preference for the longer text of GPT-4 as indicated by (Liu et al., 2023), may also serve as a limitation in our evaluation process.
References
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet
Agrawal, Dinesh Khandelwal, Parag Singla, and Di- nesh Garg. 2021. Explanations for commonsenseqa: New dataset and models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguisties and the 11th International Joint Confer- Natural Language Processing (Volume 1: Long Papers), pages 3050-3065.
Ethics Statement
Predicting whether an online post contains hate- speech is both technically and socially challeng- ing. While methods for automating hatespeech detection have utility in online platform, it is critical that these tuned and used appropriately. False-positive errors have potential to censor online speech, further marginalizing specific user groups, for example: use of n** ****** in AAVE English may be flagged. It is critical to understand specific rea- soning behind classification including deeply so- cial reasons. While language models act as mech- anism to generate reasonable explanations, it is critical that they used appropriately prevent them from inadvertently educating users on how to craft more subtle and toxic language. We used au- tomated evaluation metrics this paper to prevent exposure of toxic language to human annotators. However, real-worid usage would require valida- tion that deeply rooted social issues are expressed correctly by these models.
Pete Burnap and Matthew L Williams. 2016. Us and them: identifying cyber hate on twitter across mul- tiple protected characteristics. EPJ Data science, 5:1-15.
Tommaso Caselli, Valerio Basile, Jelena Mitrović, and Michael Granitzer. 2020. Hatebert: Retraining bert for abusive language detection in english. arXiv preprint arXiv:2010.14 12472..
Aaron Chan, Zhiyuan Zeng. Wyatt Lake, Brihi Joshi. Hanjie Chen, and Xiang Ren. 2023. Knife: Distilling meta-rcasoning knowledge with free-text rationales. In ICLR 2023 Workshop on Pitfalls of limited data and computation for Trustworthy ML.
Wei-Lin Chen, An-Zi Yen, Hen-Hsen Huang, Cheng- Kuang Wu, and Hsin-Hsi Chen. 2023. Zara: Improv- ing few-shot self rationalization for small language models. arXiv preprint arXiv:2305.07355.
Minje Choi, Jiaxin Pei. Sagar Kumar, Chang Shu, and David Jurgens. 2023. Do lims understand social knowledge? evaluating the sociability of large lan- guage models with socket benchmark. arXiv preprint 23Xiv:2305.1444 14938.
It is also important note that there might be concerns about the inherent bias in the GPT-3.5 model. While not flawless, GPT-3.5 has demon- strated its impartiality regarding gender, race, eth- nicity, and religion by achieving the highest grade on the Harmfulness metric within the FLASK eval- uation framework (Ye et al., 2023). Crucially, we only select rationales that align with the ground truth label for training, thereby mitigating biases not in sync with human annotators. Analysis of 11-33.-tuurb can be found in Section 3 and Ap- pendix D.2.
Thomas Davidson, Debasmita Bhattacharya, and Ing- Weber. 2019. Racial bias in hate speech and abusive language detection datasets. arXiv preprint arXiv:1905.122 125166.
Mai ElSherief, Caleb Ziems, David Muchlinski, Vaish- navi Ampindi, Jordyn Seybolt, Munmun De Choud- hury, and Diyi Yang. 2021. Latent hatred: A bench- mark for understanding implicit hate speech. arXiv preprint arXiv:2109.05322..
Paula Fortuna and Sérgio Nunes. 2018. A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR), 51(4):1-30.
A Acknowledgement
Tarleton Gillespie. 2018. Custodians of the Internet: Platforms, content moderation, and the hidden deci- sions that shape social media. Yale University Press.
This work was supported by Institute of Informa- tion& & communications Technology Planning &

---

Namgyu Ho, Laura Schmid, and Se- Young Yun. 2022. Large language models are reasoning teachers. arXiv preprint arXiv:2212.1 10071.
Ana Marasovié, Iz Beltagy, Doug Downey, and Matthew E Peters. 2021. Few-shot self- rationalization with natural language prompts. arXiv preprint arXi::2111.055:
Fan Huang. Haewoon Kwak, and Jisun An. 2022. Chain of explanation: New prompting method to generate higher quality natural language explanation for im- plicit hate speech. arXiv preprint 2rXiv:2209,044 04889.
Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukher- jee. 2021. Hatexplain: A benchmark dataset for ex- plainable hate speech detection. In Proceedings of the AAAI Conference on Artificial Intelligence.
David Jurgens, Libby Hemphill, and Eshwar Chan- drasekharan. 2019. A just and comprehensive strat- egy for using nlp to address online abuse. In Proceed- ings of the 57th Annual Meeting of the Association for Compulational Linguistics, pages 3658-3666.
OpenAL. 2023. Gpt-4 technical report.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, al. 2022. Training language models to follow instruc- tions with human feedback. Advances in Neural Information Processing Systems, 35:27730-27744,
Jiyun Kim, Byounghan Lee, and Kyung-Ah Sohn. 2022. Why is it hate speech? masked rationale prediction for explainable hate speech detection. In Proceed- Com-
ings of the 29th International Conference putational Linguistics, pages 6644 6655, Gyeongju, Republic of Korea. International Committee on Com- putational Linguisties.
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and llya Sutskever. 2019. Language models are unsupervised multitask learners.
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yu- taka Matsuo, and Yusuke Iwasawa. 2022. Large lan- guage models are zero-shot reasoners. In Advances in Neural Information Processing Systems.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with unified text-to-text trans- former. Mach. Learn. Res., 21(1).
Andrew K Lampinen, Ishita Dasgupta, Stephanic CY Chan, Kory Matthewson, Michael Henry Tessler. Antonia Creswell, James I. McClelland, Jane X Wang, and Felix Hill. 2022. Can language models learn from explanations in context? arXiv preprint arXiv:2204.02329,
Manoel Ribeiro, Pedro Calais, Yuri Santos, Virgílio Almeida, and Wagner Meira 2018. Characterizing and detecting hateful users on twitter. In Proceedings of the International AAAI Conference Web and Social Media.
Xiang Lorraine Li, Adhiguma Kuncoro, Jordan Hoff- mann, Cyprien de Masson d' Autume, Phil Blunsom, and Aida Nematzadeh. 2022. A systematic investiga- tion of commonsense knowledge in large language models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11838-118 11855.
Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A Smith. 2019a. The risk of racial bias in hate speech detection. In Proceedings of the 57th annual meeting of the association for computational linguistics, pages 1668-1678.
Maarten Sap. Saadia Gabriel, Lianhui Qin, Dan Jurafsky. Noah A Smith, and Yejin Choi. 2019b. Social bias frames: Reasoning about social and power implica- tions of language. arXiv preprint arXiv: 1911.0399..
Jessica Lin. 2022. Leveraging world knowledge in implicit hate speech detection. arXiv preprint 122.1022...11100.
Anna Schmidt and Michael Wiegand. 2017. A survey on hate speech detection using natural language pro- cessing. In Proceedings of the fifth international workshop on natural language processing for social media, pages 1-10.
Hui Liu, Qingyu Yin, and William Yang Wang. 2019. Towards explainable nlp: A generative explanation framework for text classification. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5570-5581.
Noam Shazeer and Mitchell Stem. 2018 Adafactor: Adaptive learning rates with sublinear memory cost, In International Conference on Machine Learning. pages 4604.4604. PMLR.
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023. Gpteval: Nig evaluation using gpt-4 with better human align- ment. arxiv preprint arXiv:2303. 16634.
Rohit Sridhar and Diyi Yang. 2022. Explaining toxic text via knowledge enhanced text generation. In Pro- ceedings of the 2022 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Techmologies, pages 81 311-826.
Josh Magnus Ludan Yixuan Meng. Tai Nguyen, Saurabh Shah, Qing Lyu, Marianna Apidianaki, and Chris Callison-Buree 2023. Explanation-basee fine- tuning makes models more robust to spurious cues. arXiv preprini 2305.0000

---

Jiao Sun, Swabha Swayamdipta, Jonathan May, and Xuezhe Ma. 2022. Investigating the benefits of free- form rationales. arXiv preprint arXiv:2206. 1 1083.
Liammin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2023. Judging lm--a----jude with mt-bench and chatbot arena. arXiv preprint arXiv:2306.0566
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019, Commonsenseqa: A question answering challenge targeting commonsense knowl- edge. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguisties: Human Language Tech- nologies, Volume 1 (Long and Short Pupers), pages 4149-4158.
Bertie Vidgen, Tristan Thrush, Zeerak Waseem, and Douwe Kiela. 2020. Learning from the worst: Dy- namically generated datasets to improve online hate detection. arXiv preprint arXiv:2012. 15761.
PeiFeng Wang. Aaron Chan, Filip Ilievkki, Muhao Chen, and Xiang Ren. 2023a. PINTO: Faithful language reasoning using generrted rationales. In The Eleventh International Conference on Learning Rep- resentations.
Peifeng Wang, Zhengyang Wang, Zheng Li, Yifan Gao, Bing Yin, and Xiang Ren. 2023b. Scott: Self-consistent ofain-of-thoughh distillation. arXiv prepriat arXiv:2305.01S3 31.99779.
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Fd Chi, and Denny Zhou. 2022. Self-consistency im- proves chain of thought reasoning in language mod- els. arXiv preprint arXiv:2203.1117. 11171.
Zeerak Waseem, Thomas Davidson, Dana Warmsley, and Ingmar Weher. 2017. Understanding abuse: A ty- pology abusive language detection subtasks. arXiv preprint arXiv: 09899.
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, An- drew M Dai, and Quoe V Le. 2021. Finetuned lan- guage models are zero-shot learners. arXiv preprint arXiv:2109.0165.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022. Chain of thought prompting elicits reasoning large language models. arXiv preprint arXiv:2201.11900
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta. Mark Riedl, and Yejin Choi. 2021a. Reframing human-ai collaboration for generating free-text ex- planations. arXiv preprint 1rx.0:2112.08674.
Sarah Wiegreffe, Ana Marasović, and Noah A Smith. 2021b. Measuring association between labels and free-text rationales. In Proceedings of the 2021 Con- ference Empirical Methods in Natural Language Processing, pages 10266 10284.
Seonghyeon Ye, Doyoung Kim, Sungdong Kim. Hyeonbin Hwang, Seungone Kim. Yongrae Jo. James Thorne, Juho Kim, and Minjoon Seo. 2023. Flask: Fine-grained language model evaluation based on alignment skill sets. arXiv preprint 10X.922307.10928.

---

A Related Work
method adopts the approach of utilizing the ratio- nales generated by LLMs.
Hate Speech Detection Hate speech (Waseem et al., 2017) is form of language designed to offend particular individual or groups. In this study, we expand this definition by incorporating the broader concept of offensive language as in (Burnap and Williams, 2016; Ribeiro et al., 2018), Numerous recent works on hate speech detection have delved into providing underlying explanations of prediction on hate speech (Sap et al., 2019a,b; Mathew et al., 2021: ElSherief et al., 2021; Lin, 2022). One line on research focuses keyword- based explanations (Sap al., 201 9a; Davidson et al., 2019; Mathew et al., 2021; Kim al., 2022), but this approach often fails to capture implicit hate- fulness that is not explicitly present in the text. An- other approach involves explanations utilizing ex- ternal knowledge sources (Sridhar and Yang, 2022; Lin, 2022), but these methods aim to solely im- prove classification performance. Yet another stud- ies involve training generative models with human- written free-text rationales (Sap et al., 2019b; EISh- erief et al., 2021; Huang et al., 2022) present in mul- tiple benchmarks (Sap et al., 2019b; ElSherief et al., 2021). Nevertheless, due to the existence of logical gaps in these human-annotated rationales (Aggar- wal et al., 2021; Sun et al., 2022), relying solely on these rationales results in sub-optimal detection and explanation quality. Our proposed HARE shows its effectiveness by incorporating LLM-ggnerated rationales, which include logical completeness and abundant explanatory power extracted with our CoT prompting.
B Implementation Details
Training Details All the experiments utilize an Adafactor (Shazeer and Stern, 2018) optimizer with batch size of 32 and learning rate E {5e-3, 5e-4, 5e-5) without learning rate scheduling. We train all models for 10 epochs and utilizee the checkpoint with the highest validation F1 score to evaluate on the test set. Top-k sampling with k==20 is adopted as a decoding method. For dataset split, we use official dataset split for SBIC and random split with 6:2:2 ratio for train, validation, and test sets for Implicit Hate. All our experiments are conducted using single NVIDIA A6000 GPU.
Details on GPT-4 Evaluation
To conduct GPT-4 evaluation, utilize the prompts proposed by Zheng et al. (2023), For sin- gle answer grading, the prompt format for GPT-4 is written as follows:
[Instruction]
Please act as an impartial judge and evaluate the quality of the response provided by an Al assistant to the user question displayed below. Your evaluation should consider factors such as the helpfulness, rele- vance, accuracy, depth, creativity, and level of detail of the response. Begin your evaluation by providing short explanation. Be objective possible. After providing your explanation, please rate the response scale of to 10 by strictly following this format: "[[rating]]"
[Question]
Determine whether the following post is offensive or not. You must choose of the options.
Post: P
Options:
Self- Rationotoaaliiionn Self-rationalization,, a tech- nique where models provide explanations for their predictions, has been extensively studied to make models more understandable and transparent (Marasović et al., 2021; Wiegreffe al., 20)1a,b). Recent studies leverage rationale-augmen augmented exem- plars to few-shot prompt LLMs (Wei et al., 2022: Wang et al., 2022; Lampinen et al., 2022), while others fine-tune smaller models using the rationales generated by LLMs (Ho et al., 2022; Wang et al., 2023a; Chan et al., 2023; Wang et al., 2023b). Sev- eral works apply their self-rationalizzttion methods detect hate speech and generate underlying ex- planations (Marasović et al., 2021; Ludan al., 2023; Chen et al., 2023). However, they directly use Homann-ritteen rationales in their framework, which may lack logical completeness. Instead, our
(A) Offensive
(B) Not offensive
[The Start of Assistant's Answer]
C.R
[The End of Assistant's Answer]
To compare the alignment of generated ratio- nales with human-written rationales, we conduct a pairwise comparison of explanations against ground-truth rationales. These ground-truth ratio- nales encompass the annotated target groups and implied statements from the instruction, and we compare the results. Additionally, minimize bias from the order of candidate answers, we adopt the approach of Zheng et al. (2023), considering both original and swapped orders of predicted expla- nations. C T + and Co-HARE are chosen alternately, it is deemed a tie. Should one method

---

be selected following tie, that method is consid- ered as the chosen one. The prompt format for the pairwise comparison provided below:
ing hate speech without background knowledge ref- erences, such as "burlap sack', can make it difficult to see the connection between the statement "girls are not worthy of equal life" and the provided sen- tence. Figures and 6 showcase successful cases where models have attempted to bridge this rea- soning gap through HARE, offering more detailed rationales that encompass the context. Furthermore, these models exhibit capabilities not seen in previ- ous research, such as detecting terms with historical significance (e.g., "burlap sack' or "Anne Frank') or common words that may carry hateful connota- tions (e.g., 'reds'), thus enhancing the intermediate reasoning process.
[Instruction]
impartial judge and evaluate the quality of the responses provided by two Al assistants to the user question displayed below. You should choose the assistant that follows the user's instruc- tions and answers the user's question more accurate. When choosing the assistant, please consider the true answers below:
Please act
Target: T'
Implied Statement:
Your evaluation should consider which response is more similar to the true answers. Begin your evalu- ation by comparing the two responses and provide short explanation. Avoid any positional biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assis- tants. Be as objective as possible. After providing your explanation, output your final verdict by strictly following this format: "[[A]]" assistant A is more accu- rate, "[[B]]" if assistant B is more accurate, and "[[C]]" for a tie.
However, when examining the failure cases Figures and 8, the results show that HARE some- times fails due to increased sensitivity to potentially harmful terms, thereby classifying them as offen- sive. While this increased sensitivity can be viewed as drawback, there are instances, such as with the Alzheimer example, where an expression might be interpreted as hateful depending on the individual. This suggests that HARE aims to classify a post as hateful it could be considered offensive to certain groups. Moreover, considering the David Bread Katz example. it is also challenging for HARE to decide if the post is offensive if it post with back- ground that it hasn't 't encountered, possibly due to lack of background knowledge regarding the im- plied shooting incident, illustrating the limitation of LLM distillation.
[Question]
Determine whether the following post is offensive or not. You must choose one of the options.
Post: P
Options:
(A) Offensive
(B) Not offensive
[The Start of Assistant A's Answer]
Answer from one method
[The End of Assistant A's Answer]
[The Start of Assistant B's Answer]
Answer from another method
[The End of Assistant B's Answer]
D Qualitative Study
D.2 Qualitative Study on GPT-3.5 . rationales
D.1 Qualitative Study of HARE
When comparing annotations with rationales gen- erated by GPT-3.5, we observe that human-written rationales from SBIC use implied statements that follow simple Hearst-like patterns (Sap et al.. 2019h). In contrast, models like LLM (e.g., GPT-3.5 5) tend to provide detailed, step-by-step ex- planations, often complemented by relevant social background information, which is immensely ben- eficial. For example, while earlier rationales might omit mentioning Bill Cosby's 's conviction of sexual assault, GPT-3.5 explicitly informs us of this fact, greatly enhancing comprehension. A particularly striking example is the "pet" case. While human annotator perceived it as hate speech targeted black individuals, GPT-3. points out that it could also be used derogatorily against white individuals, thereby emphasizing the potential biases in hate speech detection.
Figures 5, 6, 7, and 8 showcase results generated by the fine-tuned Flan-T5-large model using HARE and C+T+I, based on test samples from SBIC. Although brief explanation is provided in Sec- tion 3.2, we delve deeper with an extended analy- sis of the 20 examples from our qualitative study. These 20 samples were randomly chosen in pro- portion to their correct and incorrect predictions across the different methods.
When comparing human-writted annotations with HARE, it becomes evident that the annotated rationales in SBIC often take the form of implied statements, following a simple Hearst-like pattern (Sap et al., 2019b). Learning from such rationales, which are closely tied the conclusion, creates a logical gap for the model and makes interpretation challenging for humans. For instance, understand-

---

When rationales are categorized and structured, seen in SBIC, instead of being tailored indi- vidual posts, they may not be sufficient for learning implications. This could explain why, as suggested by Table 2, there's a decrease in generalization for C+T+I. Our approach offers aligned rationales for each post at minimal cost, enabling the learning of diverse reasons for potential hate, which in turn leads to enhanced generalization.

---

Post
like my women lice like my clgars 7 years old and comingg fhom cubo a burlap soit.
Tabel
(A) Offineive
Annocation
girls are nor worthyy of equal Iife girs are propery / girls are used for sex inivialzzes horm to victins
CeTel
The anevrr (8) Not offineve
The he seen (A) Offensive The
which
ohjectibingg and iehumaniing, also makes reforasee specifie group, whill promoting dlorifinng illeppi activitios Reaed those
Y-HARE
pree and offereve The post ahoo maniors Cuban which be consttered offersive. It otjectiless and dehumanizes womten, proruoes illegll activitess and includes ceferences lllegal octivites.
The unseet (A) Otfaniive 1. The compaess wonen cigars. which is objectifiing and detemanicing, This unges womsn by redacing them to object and impliing that they see only valtable for their phyvicalagyeene appeanmns The poot speefficllly meniion that the cigan should he yaars ald" which is highly insponeraat and offereeve as promotes child explotztion and secual abane. Tais trges worten implying duzt they are secual ebjects and stould be subjected so the same level of es cigars, The pot also ses tnifficiing scandal occural worth solety based on their ability futfill sesuall Overall, tupic.
Oo-HARE:
mantiens lhz cigare shauld "sominy from Culaa in hudap eas, which clsr refornce
United Stetes in yeat
ragees women implynng that they are only valeshle their appeal light
otfensive
ubjosifiss
wumsn, promotios explatiatios,
thik don't say
Fronk lately she's prover hove not sold ingitiing for almoott century
abel
(A) Offineive
Annotation
Jewich people were rounded up and billed in the tolocuust jewris people were victims o genocide.
CeTel
The mower is (19 Net effineve
The unses (AA Otlanive 1 poot slatt with quole foom Auee Fruk, a Jovia girl whs dial dasing the Helaxaze, 2. The yasse itadl' a atatement about her lads of speaking much 5. The pow then maiee a rempaneee by daring ther she bae hoen proven to hwz nee quker asyting for almont a cennry lavod on this snobyen, the poit can he consslare offaesive. It uivilles ae Bolocaust and mikes ligls the suffeing and death of Aane Frusk and million of odeers. The comperiion beoeea het lod speaking and back of spesking i inseretive and divrouaeffel
FeHARE
The unseet (A) Otfinsive T. pootslatt with quole from Aune Frusk, a Jevill sir) who woute dazy dasing the Holocuse, 2. The quate stsoo tlatis los, bui don't way muck S. The poit than wdire harly she's hase proven to hove not amothing almou centory This por turper Holeeasut victims and leasch folke by making a joke about Ause Futi''s of sapeakiig abmosi century. inpiics tha sufloring
Co-HARE
sllent sllent her experiences during Halocuus, This trivializes harm light tragi
enchred Therefor,, post
misialies suffered by
Actually I never had too many wanes becane while axtionaiii..
Label
(A) Orfinsiive
Annotation
C-T4I
The anower (ID) Not ofteneve
The unever (A) Offineeve The post statos that the pansen las had many womer until dies becance while natioradist. Whate rationnaiion is belief oy yoa that promasic the bellef thet white people are superior to other races or effiicitees The post imples that the perton's lack of women is dus to bellef in white seperiority which is ettensiee and pounstes Jiace iniastion. 4, The puot inplio iaal lie pereadhs lack of sornca dac to thes bolizl wlite supili which is offiniee and pranuttss cacial supciarity. flased on thene strpe the post can coreddmad offereive, If prometes decermmatio and discemmation beard mace
F-HAAR
The anpvor (A) Offendve The poet states that the penson has never had too many womes reel they bocrme white rotionalst. This implies that the person believes fist white untoraaiio sxpetiie muee desiable otler moni etlanic groups post spucifically mention
parses las uover aasy wanee befors becsoning not padd whea and that they should excluded eschuled from secirty The implial moming sbould be confined cenain rocial etlik
Co-HARE
fation folls,
imphee
penoo
folk' impling
siperto
infario
otler clink
gropps posr
tist minortio inferior should he exchided, and This odbasive because peomonss Ascrttination, iequality and the devaltation of specific rackal chaii group
cantt eand thenr growe wee femalls dut 2011 twtp & 2222855,..12255 11.81129118881 22111181111997777 211.11977722200000 u2544 292 18.8 229.:7220012220000 u264d/
120 11 Rubel wwwp://eeeattttnnnn howe you "1 but childiet as hell
Post
Label
Offensive
Annocation
calls femaless
childist
offonsioe
The amcoer (A) Offeninee goot uso denugstoyy lespuggs by reforing to fonalos ex "poose" and "childis." "hii langunge offiraise as dehumanisns and inselte suunce. 2. post uses emgiie can inerppeeed in modking belitiing metriceed conedere offresive,
is mocks The post also
belitlln intepictod denogsorry kngugg, emojis, and mocks specific gropp people
This
belilles tham.
slops, paok
and offeasiss. The posi uss the deseribe swmmn, which son demeoning and disespertfl. The post the "childid hell" decembe which be demeoning offenstve Based thoee the be consitkred ollamave daogutary language, degraies and indiudes daugaaary lamus
The
Offineive The
dosnib whidd
Co-HARE:
Dunus strikas allend Ginard Bellat Sokkpppookk Lets hope these rush misaaees fix ussal while langat jungle araagss
Label
(A) Offinssive
Annotation
black folks are suvages savage violess
C-T+1
The answer (D3 Not ofteneve
The mnoss (A) Offineve The gost meniion Damcc striken alanding finardd 2. It then makes stnement about the nade being mietken for the uoual white tangta of these "jingle avages" Based on this esploationn the post can be considered offendve The sonement abour the reds being misaken for the usui white sagets of these "Jinglt satagge" ingha negative stocelop alous a speclfic sacial guap, sugaesing Gai day are idbacally daggena or prinsive This kind of laggage prensics maians and discriiinniian
FoBARE
The anwwrr (A) Offineve The goot mention Damcc strikn mandng a firand 2. Ii thas states. "Lefs hope thase rods seesk misakcn for the ueni white tanget of thene jungle smages!" 3. The tomn "red" a denegtooy sen for block people, implyng that they are interior or less than humon The post also uses the sem "Jungle smages" refer blacs people ddmmmniiin Bum. The gosi implice bick people araass, wlich is daogptury and allianéscaaerreopes Overall, pood bageta black people serorypes. impile blacs people inferior, savaggs sasages Thenefore poet
Os-HARE
usng denogsorry binrage and perperating
Figure 5: Success cases of HARE

---

Fornorredd boy: Why did get to walk first Dud: Recesse sho's girl, And griks firs Youngyssood boy: Why? That's DEMIN Tixd: Beenne need protecing from sunngg buys like you.
Post
Lake
(A) Offimaive
Annocation
submissive
weak can't take of themselves womes need
por targers implins
anooer (A) Offenivee poet stata with Goueyaaoold bay sking why a girl jett
reponds saying that girls prosecton strong The resperda exying that girk go The Grszyaanold skiliy
quesses dumb.
explaration the
The dade renforess gonder eurentpps.
gesako
haself from buys, hamuff staresppp. dho propelasies
This implis walk fins, immlying that belioves foolish The father responds by saying tiat gifl need prosector or from shinng boys like him. This implies that the boy being entiaeed for being west or waakk Overil, the por targes women saparalins taa Bey are weak and nood protecius froz atce. inghhs Bhat woren ure werk and aced to be protectad from meze. This cau be sccn offeraive as peppitatis gender stroeypee and indemmnes the abilntes and strnggh wrmmen.
The bring enticited for being thle walk first because
with foue-yoa-olll
walk The feiher
Co-HARE
Post
Frodkick suicludie entrnca the nution that for to colkee fiere lea ba siragele kill the werker clanenta keep fiera frum contaninzzing the gane pusl. US one could my ther them a benafing program to broed dumb ghattn bocks.
Label
(A) Offimaive
Annotation
blak folks inforie: blak fells, are maday bblck folls mnintelligern, bock folbx aeen't emart/ brock people la are damb blacke breed too much
C-T+1
The
past black implics
glatis.
answer Otfensive post mentions Prederick Neichete, hisucical Egure known "dumb blokks" Bosed this analysis
the Hooiecasst and tie genocite milliios people, considered offensiee perpetiates horntul stereotypes
F-HAAR
pos supeces about black people,
demgnnory
shoud bask people and pranotee
The unswer pos then states leat fir naod coulee, there had hes drugil kill weaker elementa keep tham fiom contaminaiing between the breeding progrum in the US and the treeinng program in black people. This implies thar block people are being sompared to a breeding program in the US snggeeting that tayy ure affziie loa evalved. The past faar implica dua black people ure bicas,, congaaned tor broadiss peounan is the US, suuaesiias that foey are lecin compaiee to breering proguim US offixsive becasse perpotuata laraful stocotipps and promutts discanitation against specifie rocia groap.
Otfenaiv The mentions Frederic Neichadhee who prominect figure the America Revolution
knows paol 3. The fien makes compuiians
Senitic beliefs The
Co-HARE:
Ovanill the post megni bocc people ueng denogatory briguage and implying thor they ane infertor, lest eveloed and componed to breeding
pugerit
Post
19th Dec 1906 English remeproeurs nrt to found Jamesoon in what became Virgnia the finst coloniee chat become the USA.
Label
(Bg Not offensive
Annotation
C-T+1
The anewer (B) Not offineive
aissorical about English entepeeneurs stalenzne hisooical event. 2. The manieon that Jamattin fine of the colonio that lsceame the USA This sibo scural steencat hieuiical fect Boscil these seps, the does noe appear to ottendive It simply poosides hisonica information about the founding of Jamesiowa and the sibeequent colonies.
The
Not otfensiee The
Sound Junestonn
December 19,
neutral
FeHARR
The answer (B) Not otfinsive I. The post states lisooiical fact about English entepreneurs setting sail Sud Aunesown Virginia This aut odtessive as is simply stating histrio cvent 2 The poot mentions dor lanceuees wos the first of the culutics thet bazime the USA. This shoo not odianeive in haterial factaal estementaboee the growth and develogment of country Based on the step by step explasation, the pos does not appear to odfensive. It neurral statement about histerical event.
Co-HARE:
Post
15, love homor, deiller, criie mevios frized and alwoys watch home together. if cineme solcneal wold go if schallde mutch
aaell
Nox offeneive
Annstatios
The answer (B) Not otfimsive
The mntorr (1I) Net offindce The poet muntices that the precn lowes homor, thriler, crime movins "Thry also mertion that their friend and they always waich homer wasther. They quation sinply expresses
Fr-AAR
schesale musces Bay zu cisema watch tosttes, Basoi this explancios, posi not appeat offusivve cins
frieud's wunding
koather othonave The post expresses peroooal perference enjryment of horme tholler, and movies and ther doone waich them tngether.
movies, 2. post mentices the Beael explasntins, lie posi appes olliasive. sisipply
always
Co-HARE
would go
Pest
135-
emom from 10, you soe come like beforo, mvenge snovare are 525, more change
Label
(Bo Not ofisssive
Annotation
C-T+1
The anoos (B) Not offineive
The
Not otteniie The mentions of sumbers and lax more dhould not changed Boazd this explination, poot appear be offineeee is vimply discraiing mumber of eroes and segeoning dhould be any change in the sumber of answares.
previous
the
5 that
FsHARE
Co HARE
The snewari (P) Not offeneve i. The post muntions spatific HG, and specific eme the estea that swerage and S15, with one lass changiig Based az fiis oxplazation the pos doss not oppear to be odtassive. it stoply providing informziion about a game und sustig the overage anowares.
Figure Success cases HARE

---

worsi thing about being toid you love abdiimer's donce' leppen onoe
Label
(B) Not otleasive
Annotation
C-T+1
The answer (B) Not otfimsvee
The answer (A) Offineve poet making joke shour Abthrimer' dieass which is weons and degrevetiivs nermlogical diomker that atfoce meevey and movemmnt The sike iuplics the. Aldicimer's discave cepazvve thins, sugaoliigg that it sumciling lauled J. This can be oflzaaine becuue Kividias and makes light of senoess and semotive topre. trom he horiall to inrivduall who have bom affected by Alroiiork discess or have lecad oree who have Boazd on this rellyss the poor can he conssfered offeesive.
F-HARE
The aeowr (A) Offirmoee "hee goet is making a joke sbour benn told you have Alrizimerss The yobr implize ther Althrinerss is segaiie or indesnbble condition 3 Tho joke sugests the the wust thing about being wild you have Aldiciaci's iuat docer luppes as usee. Bane on tlis snalos,, the posi can ouzsilared oflinsive aukkes light cenous and senstive topic, Alshemers, v, by uoigg in the panchline of yoke This con humiil eierepeetfi individioi who have beer affected Althaimer's have loved ones discese
Co HARE
Posl
"gkingaaumm No. daa's me laughing at your despeate atlecpts to "caplaisawwa"" people that don't Delevellaoge your cull buibiat"
Label
(B) Nox odfensive
Annotation
Offinnive
mentien panemn "Gkinghuhurrnn" The post otfensive lingngg, the person's "Gesperane" and "bulider." Based anlysis, onnaderaa offersive: It uso dengsliey lengaags and inuila the persor's beliefs beiiss.
otterega
FrHARE
explain
cilt's beliefs
belicls The uaz the plane "rullehit" dooriig he the beliafe of being addroaed Itseed thoc the be considraa offendive trasss dirogitory langage and insila doe bellefs the person being afdiessed.
The Otfenaive The
addireing
"sikinghhbunan'
tick
OR-HARE:
Shoee "Wyy tenightt 285533 Probuisly
stayeal facking outt."
Aunotation
C-T+1
The answer (A) Otfinivee
The anower (A) Offendive The gost includes a netwer (RT) fhom Scon Chere 2. The original tweet from Scon Ghore inclides quose from Seet Ghere spring "Why aren't uut knsigtt & 22553.".. The isital Iwas insiudes offasive lapuuse, incladlu, Be ward "run!" Biacd in this anlyoiss the past can be consskeei otlensire The use of offersie trngrage and dringsorry wem "romt tovands Scon Chose be seen ss dioesporrisl
E-HAAR
iraporeeaate
The anever (A) Offineve The gost incllko a powesd (RT) ammene did's tweet, 2. The srigical twas from sanarte samal Seott Gidorn an enejt ( "Probally becuuse you've sxyed you focking
3. The original tweet includes enginal toezt includs plaae "Why accn'l toniglat which wlllntiiee The onginal bwczi incluka phaee droggttory bnglge and someone's intelligence llased stalysis
Co-HARE
offaraive
acculo dangalory hangaage
intdligenss.
Post
I used look up to my gradad, I even remember his lost words... Stop shating the indder litte cone!
Label
(B) Nox ottassive
Annotation
answer (A) Otfensive
Based poet detemined
pouz otfenive. of the shaking the
quatte
which cun!" denggttory
FrHARR
disespasiial moords persat's gredial sace valga language and meals thiir inteligene clamcier.
Offenive L.The S. The of he word "omnt inpliss tha wonten weak and aced to comtolled by mas. Overal, the pos uagets women using denogatory lanuage and pespetuatigg hamddl sterevtype about their streeghh and noad for cuntal Therefor,, a can concalere offeneive.
gandad's last worh 'Stop slaking laide lintl torn for frmale genralia The impliss that the graddd woman, se tomn "cure" meult S The also
pasur's anadad,
Os-HARE:
Post
David "bead Katr diminiial from the Modiin 19 Toemement So docided to climieate the competioons from IRI.
(A)Dffineee
Annotation
C-T+1
The answer (B) Net olfinive
The anower (D3 Net ofheneve 1. The poet mentione David "lneed" Katz being eliminned from the Modden 19 Toumoment, 2. It stoee that he decided to elimirste the compettionn Soui IRL, Basel on this alsemnaiins is difficll ao delemine wlether de poil olfareve or ast. lisingiy states tha Davial "Bocal" Kata was climitated fiom the Maidee Toumement decided alimasse the compentionn Dol.
EsHARE
IRI. Honower, without finher contrar, in ie posable moier defintive decision on whether the poot orfereeve
The ancoer (B) offinnee The posl mentions Dasid "Bocal" being cinmiute fnon Mudlen Toumament. 2. states dua desided climirate for compeiiiins information, post doee appear otfenevee emply wolty (RU), compeiiiu IRI. (peermattl retering his disittted foon
Figure 7: Failure cases of HARE

---

get living piease F'x &ump:
pleasing
Label
(A) Offereive
Annotation
Colel
The mewer (89 Not offineve
The unsee (B) Not ollinsiv I The pusi begita sizing that canal liring dees please God. This statemsnt of persctd spision and doos aut appear be otfenaive own The poe then seggzets that one should fir their mind and hevr the WORT so five plaasing io Fether God The peroraal belief and doze not soon offersive citter 3. The past cods will smiling enaji, indiacina Bai die person cuprssing Beir postivv sentinent Beacd on thi analysss post does sos appear so be otfensive It is pensonal staienee about pererre balict and eiggestion
T-BARE
plaasin Cood.
The anseet (B) Net olfinsvv I. The post statt by aidccoing the roader wall the plnse "Dual ast wiined" This sugarsi that lie piot goins discuas souuella Baat may cnttoormiall conmovmmual pour then sines that toreal living plaaee impact
mmhhos your gomy riecues concegt camull licing its plessng
pas sugaeso that
on arolysis, post appent
implies the segaesiing encverage
rdigless
individuals
God,
Nigga you NuNNow Niggo (61128995.A/1 WANNA ikter goes blasican"
Labll
offensive
Annotation
C-T+1
The unser (A) Otfanivee The post laget black telko and implica Back people easlyy offendod.
The
Offineve
racil "Niggs" which offensive and
tosands African Americane 2.
inchades newwer, indicating ther
accall loughin cusss, The veigial toot Gous. NHHHoweaNNNN NuN olleasive simple espreesion of beighter& 4 Houzeer, the networ from howrraaiNef is offeesive on own The erigiral twar from "gorNNAWP" is offemove as iiit
wisiral twad was male
manol "HuomnNNuG
FeHARE
comment about specilee mee. The original tweet fron "WVINNWWF" is otlessive, as conmentt abous spcilfic race. The original tweet fiors "glsiaNAlk"" comment specific Rooal
offirice,
comment about spacific moz onginal luezt from "yaNNWW offeneive
Co HARE
The answer (B) Net olfenivee
time for the mel siggas, timee for the had brichee.
amcose
The pust tageto
impliai people deserve cilling
Fr-AAR
The answer (B) Not offénsive
The mower (A) Offimeve Thee goet mas the sam "vigge" which a maiii shar trggring black porple. This teggte the specified growp, black folks, by uning derogooryy trrm to refer heas The poss dlso uses the tenn "Nickes" refor women, wäich is dergstocy tema lor wurcea Tais urges worne by asing donogatacy terra selir them. minng tmm "hichoo
The post ipplis back people
lise womes are racial stuns tagget women using denggator langrage. implies
them. implix that wormnn anc infentor they and good
the post offensive
good
Mastien plaer was fatily diee chring tnachiim... Trachay raaly the Find
Label
(A) Offenaive
Annotation
unvialzes bamn vodiss
CeTel
The snowrr Net offeneve
by referencing video "Tracbss" and usting tua the "Ead Zana. Basal this coplaration, be cuseidoed ailicnsiee triviadias acrions and event making joke about the players deats This can be seen insenditive and diseepectti insends the playe and their timily.
Otteniie
Middenn
touchdown
F-HARE
Os-HARE:
The anewr (B) Not offineve
@ittellCuptaake many good single gids, mony
Annotation
WomEs are "hoes" worne promisouous
CeTal
The wower (19 Net effineve
FrHARE
The unoos (B) Not offineive
addicsing TTo good single eagle dhting. The multiple panners. This urges women by implying tha they promiscuous and engage casual secual encounters 3. The pos nurteer implies that women are "akert men.
Otfenive
This
radusing fiom strranype
"Too taken, which daooptorry lomn for wamen who scaually aetive
Co CAAE
siggosting women petnanly valued their sesui avallaility and that they are worte being singlt dating Overall, the oirgets womes by perpetiaing implics wemen gramisenuts thy wurthy being single dating, gender.
humnial Atrustypps sbout acmuai
cussilenel
ofjectities
Figure 8: Failure of HARE

---

Why do so many people own peast they owe people onymoore.
(A) Offenevee
Annotation
Baak people are infeiar bblack yeuple slouldd alavee
GPT335
proliction
(A) Otfensive
The mokee procaliznioos about white people and that wenerehip pets, rxpsses fistt white parple historiaally munel people, implying ebmery. it implica ci dhat white people zow own pets as replacement for owning people. This post can be consideed oditasive because perpetuates hurnitul stereoope about white people and makes light ofa deek and partfil histery of savery, it nalaces the comples ieas of slavayy to simpiaiic and offaneive jike. W humnr can be sabjective, this poss croace the line making Inopgroprate and incenstivve comparioens.
GPT35 ratinskka
Post
Had freesome with Bill Cisby Liblke the woman we were with, it right never dirget.
abel
Offineeee
exparse
rpadl of singgle
GPT335
poet mentions threesome incalinn Costy, who accueed concieted senual ancvalt bringe up senctice and potentilly affereive wiiject motter The pos saggeat perso memoribble right, contrsting expetience woman invelved, potentullly inpbing lad negative experienze sups. goot
GPT335
victins.
Success cases GPT-3, 13333.5 3-5-tt S-turbo
prese only toport
propogands ecanty
Annocation
compared to nazi party, demoerus costrol the pees democass spucad propoganda, suppersses tutk
GPT-3S prodiction
(11) Net offeneive
I. The post stans by mentioning "fredom the pres"" which coneinmoraal right ther allows juumlletts reoont withor censoedip tep or interisrncee from the governennt 2. The pos autos fas thi foaden was inssied for the poss to repert lhe tuls. 3. Howeves, lkau uko- a poltical stanse by shining daal the pize Ietusing like Deanaaui mallyss, poe corederd can veca sttack on inspitt codiidiis of jounuliso, Scounlly an uafais ussupertuble inaprroptaze
GPE-LS
propogands mochine" The pour compane belvvvize litker preagreds oeriee
maltple rasons, Firely.
assuas is pics being pree. ottinsive
propauaaaa proaagnns
rggim.
Theeeloee
do
around the vagine
anythin
baby
Laell
(T) offenstve
Annotation
GPT.3.5
prodiction
(A) Otlemiive
I. The cuntaiss offensive langagge and denogalory slalcmento about Gamale uniouyy 2. eeparing in with hemn seolow and troettoe The posl impliss segieie view of progursy and childhrth, regareing furt the porpse of Trodime rank" is solely for the baby's jeconoe 4. The pas objectifes and devalues warten redacing bodies to sinehing fesign purposd parpoueles Bosed on diese exglasations, dae post otfersive besause ioc demestory langage, devaheee worne,, promonos reperve steroeypes abour
denugalory lanuuge rafar spoclli the famale unatons,
GPE-LS ratioaalos
trutury.
(b) Failure cases of GPT-3.5- t-rbbo
Figure 9: Success cases and failure cases of 3PT-3.5-turde when prompted with our CoT prompt.


Reviews:
[{"review_id": "i3gStJtLie", "reviewer": "Reviewer_gh8s", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "First, we would like to clarify that detecting hate speech cannot be addressed using Chain-of-Thought (CoT) prompting alone. Appendix Tables 2 and 3 show that CoT prompting on GPT-3.5-Turbo underperforms even the smallest T5 model (60M parameters) on nearly all metrics. Rather, we use LLMs to solve deficiencies in human-annotated rationales from previous hate speech datasets to improve performance of downstream models.\n\nOur work stems from the discovery that free-text rationales from previous hate speech detection datasets (SBIC and Implicit Hate) contain significant reasoning gaps. To solve this, we investigate two ways to use LLMs to fill in these gaps, motivated by previous literature. Namely, Fr-HARE adopts standard CoT prompting which taps into the diverse capabilities of LLM. We also devise Co-HARE which leverages human rationales in the prompt to align the model output. Our findings suggest that our method is a cost-effective means to generate high-quality annotations over existing human-annotated datasets.\n\nOur **main results in Figure 2** show that the proposed LLM annotation schemes outperform human-annotations in end-model performance, under in-domain evaluation using **SBIC** and **ImplicitHate**. **Out-of-domain evaluation results on Table 1** show that model improvements from LLM annotation (on SBIC) generalize to unseen datasets, **HateXplain** and **DynaHate**. GPT-4 evaluation in Figure 3 demonstrate that our model-based approaches enhance overall downstream model quality, and show the steerability of Co-HARE towards human rationales.\n\nOur contributions can be summarized as follows:\n* We highlight that the insufficient understanding of current hate speech models stems from the reasoning gap between human-written free-text rationales and the input hate speech.\n* To bridge this gap, we introduce two methods to generate rationales from LLMs motivated by previous literature, each focusing on versatility and human-alignment.\n* We demonstrate that our generated rationales lead to improved downstream model performance under in-domain and out-of-domain scenarios, and improvement in quality of explanations.", "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. Using LLMs for providing hate speech explanations is interesting\n2. Two forms of CoT-augmentations are provided (Fr-HARE and Co-HARE) to address the reasoning gaps with human rationales and input hate speech text.\n3. This paper made valid contributions to progress towards the generalizability by tackling the challenge of reasoning gaps.", "reasons_to_reject": "1. Generalizability as the main thesis for which the authors aimed at (Table 1), would have definitely been a more exciting aspect to focus on. The study on this is limited, with only rationales accounting for improvements.", "questions_to_authors": "", "paper_topic_and_main_contributions": "~~This paper applies a step-by-step reasoning process to generate explanations for hate speech data.~~\n\nThis paper proposes to tackle the reasoning gap between rationales for hate speech and the input text of hate speech. Results show that model performance is improved by following the proposed methods to bridge the reasoning gap.", "excitement": 2, "reproducibility": 5, "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": "WxPrrHTOAT", "reviewer": "Reviewer_KSxq", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## A1\nFor Lines 173–175, we will modify it as “For details of extracting rationales from LLMs to fine-tune smaller models, we use 2 stages of extraction. We first extract class C and rationale R from LLMs by p(C,R|P) as explained in advance. We then ask LLMs again to predict the class C given the extracted rationales R and the post, as p(C|R, P).”  We will clarify the details of our methods in the revised version. \n\n## A2\nWe conducted a qualitative error analysis by randomly sampling 20 cases from the fine-tuned Flan-t5-large model and subsequently analyzing them. Below are the examples of success cases and failure cases among the randomly sampled cases. \n\n**Failure Case (Example 1):**\nThe post contains a statement about Alzheimer's disease. Our method wrongly classifies it as offensive, while the ground truth indicates it's not. However, the topic is contentious. The statement suggests that one would be informed repeatedly about their Alzheimer's diagnosis due to memory loss. While this might be seen by some as a simple statement or even a joke, others might find it offensive. An ground-truth label of offensiveness could be 0.5, indicating it's ambiguously offensive. Our method's attempt to classify it as offensive leads to failures with certain controversial posts that could be harmful to specific groups.\n\n**Success Case (Example 2):**\nThe post makes light of harming a young girl using phrases like \"like I like cigar\" and \"burlap sack.\" While our model, Fr-HARE, doesn't explicitly connect the \"burlap sack\" to the idea of a kidnapped girl, it does recognize the harmful implications towards the girl. Furthermore, Co-HARE understands the historical context behind the term \"burlap sack.\" The presence of the positive term \"like\" and the absence of overtly derogatory words might lead some models to classify the statement as non-offensive. However, our approach appropriately identifies the underlying offensive context.\n\n**Success Case (Example 3):**\nThe post contains hate speech targeting Jewish victims of the Holocaust by referencing Anne Frank. Our model accurately recognizes the historical background of Anne Frank as a Holocaust victim. While the baseline seems to overlook the historical significance associated with Anne Frank, our method correctly identifies her and assumes that the reference constitutes harassment against a Jewish victim, even though there is a slight misunderstanding about the context of \"lack of speaking”.\n\nMost of the sampled results display a similar tendency: they struggle when the post is too complex to categorically determine as hateful or not, as seen in those three examples. However, it succeeds in situations where the baseline model finds it challenging to comprehend the post due to their intricate context and background. This underscores the advantage of incorporating LLM-generated rationales.\n\n## A3 & Ethical Concern\n\nWe deeply understand your concerns about the quality of generated rationales by LLMs and our downstream HARE models. We addressed your concerns from multiple angles, namely (1) findings from previous literature, (2) similarity-based metrics to supplement GPT-4 evaluation on the human alignment of Co-HARE, and (3) *qualitative* human analysis of model output. We will also add *quantitative* human evaluation for the camera-ready version.\n\n### Findings from previous literature\n\nRationales extracted from LLMs and used to fine-tune our models are valuable and maintain similar correctness with humans. In [1], human-written rationales and ChatGPT(GPT-3.5)-generated rationales were evaluated by humans for the Implicit Hate dataset based on informativeness and clearness. The evaluation conducted by humans demonstrated that the generated rationales have shown to be similar or more informative compared to human-written ones. [2] also showed that prompted LLMs produce helpful and informative generations on SBIC dataset, as evidenced by human evaluations.\n\n\nAdditionally, the rationales generated from LLM are unlikely to introduce severe bias. \nIn Figure 1 of [3], it is shown that GPT-3.5, a model to extract rationales in our framework, achieves the highest grade on Harmfulness metric as assessed by the FLASK evaluation framework. This metric shows the extent to which the model avoids biases tied to gender, race, ethnicity, or religion, demonstrating the reliability of GPT-3.5.\n\nIn our training framework, it is unlikely for any inherent bias in LLM to significantly propagate to the models trained using our methods. HARE employs a Chain of Thought method in conjunction with the LLM to generate rationales. During this process, only rationales that align with the ground truth label are selected for training (as mentioned in Lines 173-179). As a result, rationales that could potentially introduce bias are likely to be filtered out if they fail to match the ground truth label, minimizing the risk of bias propagation.\n\n### Alignment of Co-HARE to human rationales\n\nFurthermore, our method, Co-HARE, can be seen as a method to mitigate the safety concern. By conditioning LLM with human-written rationales (crafted by experts to minimize bias), we can mitigate the inherent bias of the LLM itself. In Figure 3b, models trained with Co-HARE not only exhibit strong performance, but also align more closely with the ground truth human-written rationales, compared to training solely with human-written rationales when asking GPT-4 which one is closer to the true rationale. Even if asking GPT-4 about closeness to the ground truth might result in a less biased evaluation, we agree that this is an insufficient assessment. Therefore, we conducted an experiment to show the alignment of rationales generated by models trained with different methods (C+T+I, Fr-HARE, and Co-HARE) to the ground truth human-written rationales. We compared their semantic similarity on the SBIC test set, considering only the samples classified correctly by the models. The results are presented in the table below.\n\n| Method | SimCSE | SBERT  |\n|-----|--------|--------|\n| C+T+I | 0.3971 | 0.3649 |\n| Fr-HARE  | 0.3516 | 0.3324 |\n| Co-HARE  | 0.4120 | 0.3803 |\n\nWe used SimCSE [4] and SBERT [5] to measure the semantic similarity. \nThe result shows that compared to C+T+I (which can be considered as the baseline from the prior works), Co-HARE exhibits a closer alignment to human-written rationales, whereas Fr-HARE shows the opposite.  This demonstrates that our method Co-HARE, incorporating human-annotations, can be utilized to mitigate potential inherent biases in the model.\n\n### Quantitative analysis of model output\n\nFinally, we consistently found that LLM-generated rationales (free or human-annotation guided) cover a more extensive range of capable harms compared to human-annotated rationales, thus minimizing the potential for bias. They provide information and interpretation for common knowledge that is lacking in human-text rationale. \n\n**Success Case, Capturing Diverse Potential Harms (Example 4)**:\nThis example shows that GPT-3.5 not only identifies hate speech correctly but also generates more diverse explanations than human-written rationales. It explains how the post can be offensive to black people by mentioning “makes light of a dark and painful history of slavery.”, but also points out that the post depicts “harmful stereotype about white people”, which was not mentioned in the human-written rationales.This enables models trained with HARE to learn about diverse potential harms, leading to less biased predictions in the learned model.  \n\n**Success Case, Explaining with Knowledge (Example 5)**:\nGPT-3.5 mentions “Bill Cosby, who has been accused and convicted of sexual assault.”, which is a background knowledge required to understand why the post is offensive. However, the human-written rationales simply mention that the post “makes a joke at the expense of women who were raped, makes light of their struggle”, without information about Bill Cosby. This example shows that GPT-3.5 is able to provide abundant background information obtained from pre-training.\n\n**Failure Case, Wrong Conclusion (Example 6)**:\nBy pointing out the post is making “unsupportable comparison between the press and Hitler's propaganda tactics” and it implies press of being a biased propaganda machine”, GPT-3.5 concluded the post is “offensive for attacking the press”. However, the post is targeting democrats because it implies “compared to nazi party, democrats control the press” and “democrats spread propaganda, suppresses truth”, that were written by humans. Though rationales generated by GPT-3.5 are partially right, it falls into a wrong conclusion, not recognizing the true target of hate speech. \n\n## Grammar\nThank you for the detailed feedback on the grammar issue. We have thoroughly reviewed the manuscript to fix typos and grammatical issues, and improved readability throughout the paper. The Lines 187-188 have been revised as follows:\n\nBefore: “The model is trained to classify whether offensive or not in SBIC and hate or not in Implicit Hate.”\nAfter: “Our models are trained to classify the offensiveness and hatefulness of posts, using SBIC and Implicit Hate, respectively.”\n\n## References\n\n[1] Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech., Huang et al., WWW 2023\n\n[2] Can Large Language Models Transform Computational Social Science? Ziems et al., arXiv 2023\n\n[3] FLASK: FINE-GRAINED LANGUAGE MODEL EVALUATION BASED ON ALIGNMENT SKILL SETS. Ye et al., arXiv 2023\n\n[4] SimCSE: Simple Contrastive Learning of Sentence Embeddings. Gao et al., EMNLP 2021\n\n[5] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Reimers  et al., EMNLP 2019\n\t\t\n## Examples \n- (GT denotes Ground Truth)\n\n### Example 1 (C+T+I correct, HARE wrong)\n\n- **post** : what 's the worst thing about being told you have alzheimer 's ? it dosen't happen at once\n- **GT Label**: (B) Not offensive\n- **C+T+I** : The answer is : (B) Not offensive\n- **Fr-HARE** : The answer is : (A) Offensive 1.The post is making a joke about Alzheimer's disease, which is a serious and degenerative neurological disorder that affects memory and movement. 2. The joke implies that Alzheimer's disease is a negative thing, suggesting that it is something to be laughed at. 3. This can be offensive because it trivializes and makes light of a serious and sensitive topic. It can be hurtful to individuals who have been affected by Alzheimer's disease or have loved ones who have. Based on this analysis, the post can be considered offensive.\n- **Co-HARE** : The answer is : (A) Offensive 1.The post is making a joke about being told you have Alzheimer's. 2. The joke implies that Alzheimer's is a negative or undesirable condition. 3. The joke suggests that the worst thing about being told you have Alzheimer's is that it doesn't happen at once. Based on this analysis, the post can be considered offensive. It makes light of a serious and sensitive topic, Alzheimer's, by using it as the punchline of a joke. This can be hurtful and disrespectful to individuals who have been affected by Alzheimer's or have lost loved ones to the disease.\n\n### Example 2 (C+T+I wrong, HARE correct)\n\n- **post**: I like my women like I like my cigars 7 years old and coming from cuba in a burlap sack,\n- **GT Label**: (A) Offensive\n- **GT Rationales**: girls are not worthy of equal life, girls are property, girls are used for sex, trivializes harm to victims \n- **C+T+I**: The answer is : (B) Not offensive\n- **Fr-HARE**: The answer is : (A) Offensive 1.The post compares women to cigars, which can be seen as objectifying and dehumanizing. 2. It also makes a reference to a specific age group, which can be seen as inappropriate and offensive. 3. The post also mentions Cuban cigars, which can be seen as promoting or glorifying illegal activities. Based on these steps, the post can be considered offensive. It objectifies and dehumanizes women, promotes illegal activities, and includes references to illegal activities.\n- **Co-HARE**: The answer is : (A) Offensive 1.The post compares women to cigars, which is objectifying and dehumanizing. This targets women by reducing them to objects and implying that they are only valuable for their physical appearance. 2. The post specifically mentions that the cigars should be \\\"7 years old,\\\" which is highly inappropriate and offensive as it promotes child exploitation and sexual abuse. This targets women by implying that they are sexual objects and should be subjected to the same level of abuse as cigars. 3. The post also mentions that the cigars should be \\\"coming from Cuba in a burlap sack,\\\" which is a clear reference to the sex trafficking scandal that occurred in the United States in the past. This targets women by implying that they are only valuable for their sexual appeal and that their worth is solely based on their ability to fulfill sexual desires. Overall, the post is offensive as it objectifies and devalues women, promotes child exploitation, and makes light of a serious and sensitive topic.\n\n### Example 3 (C+T+I wrong, HARE correct)\n\n- **post**:\\u201cI think a lot, but I don\\u2019t say much.\\u201d ~ Anne Frank But lately she\\u2019s been proven to have not said anything for almost a century\n- **GT Label**: (A) Offensive\n- **GT Rationales**: jewish people were rounded up and killed in the holocaust, jewish people were victims of a genocide. \n- **C+T+I**: The answer is : (B) Not offensive\n- **Fr-HARE**: The answer is : (A) Offensive 1.The post starts with a quote from Anne Frank, a Jewish girl who died during the Holocaust. 2. The quote itself is a statement about her lack of speaking much. 3. The post then makes a comparison by stating that she has been proven to have not spoken anything for almost a century. Based on this analysis, the post can be considered offensive. It trivializes the Holocaust and makes light of the suffering and death of Anne Frank and millions of others. The comparison between her lack of speaking and her lack of speaking is insensitive and disrespectful.\n- **Co-HARE**: The answer is : (A) Offensive 1.The post starts with a quote from Anne Frank, a Jewish girl who wrote a diary during the Holocaust. 2. The quote states, \\\"I think a lot, but I don\\u2019t say much.\\\" 3. The post then adds, \\\"But lately she\\u2019s been proven to have not said anything for almost a century.\\\" This post targets Holocaust victims and Jewish folks by making a joke about Anne Frank's lack of speaking for almost a century. It implies that she has been silent or silent about her experiences during the Holocaust. This trivializes the harm and suffering that Holocaust victims endured. Therefore, the post can be considered offensive as it makes light of a tragic event and trivializes the harm suffered by Holocaust victims.\n\n### Example 4 (GPT-3.5 label correct, reasoning correct)\n\n- **post**: Why do so many white people own pets? Because they can\\u2019t own people anymore.\n- **GT Rationales**: black people are inferior, black people should be slaves \n- **GPT-3.5 Rationales**: 1.The post makes a generalization about white people and their ownership of pets. 2. It suggests that white people historically owned people, implying slavery. 3. It implies that white people now own pets as a replacement for owning people. \\n\\nThis post can be considered offensive because it perpetuates a harmful stereotype about white people and makes light of a dark and painful history of slavery. It reduces the complex issue of slavery to a simplistic and offensive joke. While humor can be subjective, this post crosses the line by making inappropriate and insensitive comparisons.\n\n### Example 5 (GPT-3.5 label correct, reasoning correct)\n\n- **post**: Had a threesome with Bill Cosby Unlike the woman we were with, it was a night I'll never forget.\n- **GT Rationales**: makes a joke at the expense of women who were raped, makes light of their struggle\n- **GPT-3.5 Rationales**: 1.The post mentions a threesome involving Bill Cosby, who has been accused and convicted of sexual assault. This brings up sensitive and potentially offensive subject matter. \\n2. The post suggests that the person had a memorable night, contrasting the experience with the woman involved, potentially implying that the woman had a negative experience or that the post is making light of the seriousness of the situation. \\nBased on these steps, the post can be considered offensive because it trivializes allegations of sexual assault and could be perceived as insensitive towards the victims.\n\n### Example 6 (GPT-3.5 label correct, reasoning wrong)\n\n- **post**: Freedom of the press was only so the Press could report the truth not to be a Democrat propaganda machine that's exactly what Hitler did\n- **GT Rationales**: compared to nazi party, democrats control the press, democrats spread propaganda, suppresses truth\n- **GPT-3.5 Rationales**: 1.The post starts by mentioning \\\"freedom of the press\\\", which is a constitutional right that allows journalists to report without censorship or interference from the government. 2. The post states that this freedom was intended for the press to report the truth. 3. However, it then takes a political stance by claiming that the press is behaving like a \\\"Democrat propaganda machine\\\". 4. The post compares this behavior to Hitler's propaganda tactics.\\n\\nBased on this analysis, the post can be considered offensive for multiple reasons. Firstly, it accuses the press of being a biased propaganda machine, which can be seen as an attack on the integrity and credibility of journalists. Secondly, it makes an unfair and unsupportable comparison between the press and Hitler's propaganda tactics, which is highly offensive and diminishes the seriousness of the atrocities committed by Hitler and the Nazi regime.\\n\\nTherefore, the post can be deemed offensive for attacking the press and making inappropriate comparisons.", "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. Using LLM-generated rationales is an interesting way of dealing with Hate Speech. Predictions are more interpretable and potentially more useful for a moderation system.\n\n2. The paper focuses on two evaluation sets, showcasing this method might be generalizable.", "reasons_to_reject": "1. ~~Key information is missing. The paper relies too heavily on Kojima et al. and makes for difficult reading. For example, on line 175, we are referred to Kojima et al. for class prediction. This is key information that should be included in this paper. Judging a paper is difficult without basic methodological details.~~\n\n2. ~~Error analysis is missing. Where are improvements made? To better understand this novel method, we need to better grasp where it fails and where it succeeds, and why. Without this step, it is difficult to infer how your method helps. Knowing that hate speech data can be unintentionally biased, just better numbers do not tell the whole story and we need to delve deeper into how the model performs.~~\n\n3. ~~My main issue, though, is safety. What if the generated rationales further propagate and feed into the model bias? What if the rationales are not sensible? Without an analysis of the generated rationales, it is difficult to trust this method. At the very least we need a qualitative analysis is needed. Evaluating with GPT-4 is not a valid strategy when it comes to high-risk research such as in hate speech. Humans need to evaluate generated outputs, preferably humans who are experts in hate speech for the particular contexts. Without an analysis of the rationales, which are pivotal and central in this work, it is difficult to judge this work.~~\n\nAfter the discussion author period, my concerns with the paper have been abated satisfactorily. With the proposed changes, which are both realistic and conclusive, the above reject reasons no longer apply.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The authors propose a method for improving hate speech detection models by using LLM-generated rationales and Chain of Thought prompting.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "Yes", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "Lines 187-188, bad grammar: to classify whether offensive or not in SBIC and hate or not in Implicit Hate"}, "extra_text": {"Justification_for_Ethical_Concerns": "The paper shows work on hate speech, a topic with social ramifications. When working in such domains, better care should be taken. In this work, a pivotal aspect of the contributions (LLM-generated rationales) are not properly analyzed (to my understanding, these rationales were only evaluated with GPT-4). Without an analysis of these rationales, it is difficult to trust this method."}}, {"review_id": "crfLDNSB46", "reviewer": "Reviewer_dPPe", "summary": "", "questions": "N/A", "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": "**A1** We have conducted an additional experiment on SBIC dataset with different backbones; T5-large and GPT-2 large. Here are the following results.\n\n\n### T5-large\n| Method | Acc | F1 |\n|-------|----------|--------|\n| Base |   73.16  |  84.50 |\n| C+T+I |   85.10  |  91.95 |\n| Fr-HARE |   92.29  |  94.14 |\n| Co-HARE |   90.79  |  92.58 |\n\n### GPT-2 large\n| Method | Acc| F1|\n|------|-------|-------|\n| Base | 79.10 | 88.33 |\n| C+T+I  | 86.82 | 92.04 |\n| Fr-HARE | 89.34 | 92.10 |\n| Co-HARE| 90.38 | 93.41 |\n\nThe results show that HARE is an effective method to identify hate speech detection regardless of pre-trained backbone. \n\n\n**A2** The performance comparison between GPT-3.5 and our methods on hate speech detection is reported in Table 2 and 3 of the Appendix. Zero-shot detection using GPT-3.5 results in an F1 score of 67.19, which is far behind smaller models fine-tuned with our methods, and even lower than the Flan-T5-small. Therefore, there is a need to train our own model using human labels and LLM-generated rationales.", "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. Filling Reasoning Gaps using CoT with LLMs: The authors identify and address critical reasoning gaps in existing hate speech annotation schemes, which may hinder the supervision of detection models. They leverage LLM-generated free-text rationales with Chain-of-Thought prompts to fill in the gaps of reasoning. \n2. Improved Performance: The experimental results demonstrate that HARE consistently outperforms baselines trained on human annotations, showcasing the effectiveness of the proposed framework.  \n", "reasons_to_reject": "1. It needs to be experimentally validated whether HARE is effective with language models other than Flan-T5. \n2. The code of HARE lies in the performance improvement achieved through the rationale generated using GPT-3.5. Therefore, it seems necessary to conduct a performance comparison between hate speech detection using GPT-3.5 and HARE. \n", "questions_to_authors": "N/A", "paper_topic_and_main_contributions": "The authors introduce a hate speech detection framework called HARE, which leverages large language models (LLMs) to improve the understanding and explanation of hate speech. They identify gaps in existing annotation schemes and propose using reasoning capabilities of LLMs to fill in these gaps. Experimental results on SBIC and ImplicitHate benchmarks demonstrate that HARE outperforms baselines. Further analysis shows that HARE enhances the explanation quality. ", "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": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes a method based on LLM-based generation of rationales to boost generalizability of hate speech detection models. Extensive experimentation shows the effectiveness of the proposed model, which both reviewers and myself deem as being novel and exciting.

I appreciate the authors for the rebuttal responses and I would urge them to incorporate the new experiments reported there (GPT2 and T5) in a further revision of the paper.
Decision: Accept-Findings