youngggggg
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ToxiGen-ConPrompt

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 - en
 ---
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 - en
 ---
+# Model Card for ToxiGen-ConPrompt
 <!-- Provide a quick summary of what the model is/does. -->
+<!-- {{ model_summary | default("", true) }} -->
 ## Model Details
 {{ model_description | default("", true) }}
+<!--- **Developed by:** {{ developers | default("[More Information Needed]", true)}} -->
+<!--- **Funded by [optional]:** {{ funded_by | default("[More Information Needed]", true)}} -->
+<!--- **Shared by [optional]:** {{ shared_by | default("[More Information Needed]", true)}} -->
+- **Model type:** Feature Extraction
+- **Base Model:** BERT-base-uncased
+- **Pre-training Source:** ToxiGen
+- **Pre-training Approach:** ConPrompt
 <!-- Provide the basic links for the model. -->
+- **ConPrompt Repository:** https://github.com/youngwook06/ConPrompt
+- **ConPrompt Paper:** https://aclanthology.org/2023.findings-emnlp.731/
+## Ethical Considerations
+### Privacy Issue
+Before pre-training, we found out that some private information such as URLs exists in the machine-generated statements in ToxiGen.
+We anonymize such private information before pre-training to prevent any harm to our society.
+You can refer to the anonymization code we used in preprocess_toxigen.ipynb and we strongly emphasize to anonymize private information before using machine-generated data for pre-training.
+### Potential Misuse
+The pre-training source of ToxiGen-ConPrompt includes toxic statements.
+While we use such toxic statements on purpose to pre-train a better model for implicit hate speech detection, the pre-trained model needs careful handling.
+Here, we states some behavior that can lead to potential misuse so that our model is used for the social good rather than misued unintentionally or maliciously.
+- As our model was trained with the MLM objective, our model might generate toxic statements with its MLM head
+- As our model learned representations regarding implicit hate speeches, our model might retrieve some similar toxic statements given a toxic statement.
+While these behavior can lead to social good e.g., constructing training data for hate speech classifiers, one can potentially misuse the behaviors.
+**We strongly emphasize the need for careful handling to prevent unintentional misuse and warn against malicious exploitation of such behaviors.**
+## Citation
 **BibTeX:**
+@inproceedings{kim-etal-2023-conprompt,
+    title = "{C}on{P}rompt: Pre-training a Language Model with Machine-Generated Data for Implicit Hate Speech Detection",
+    author = "Kim, Youngwook  and
+      Park, Shinwoo  and
+      Namgoong, Youngsoo  and
+      Han, Yo-Sub",
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.findings-emnlp.731",
+    doi = "10.18653/v1/2023.findings-emnlp.731",
+    pages = "10964--10980",
+    abstract = "Implicit hate speech detection is a challenging task in text classification since no explicit cues (e.g., swear words) exist in the text. While some pre-trained language models have been developed for hate speech detection, they are not specialized in implicit hate speech. Recently, an implicit hate speech dataset with a massive number of samples has been proposed by controlling machine generation. We propose a pre-training approach, ConPrompt, to fully leverage such machine-generated data. Specifically, given a machine-generated statement, we use example statements of its origin prompt as positive samples for contrastive learning. Through pre-training with ConPrompt, we present ToxiGen-ConPrompt, a pre-trained language model for implicit hate speech detection. We conduct extensive experiments on several implicit hate speech datasets and show the superior generalization ability of ToxiGen-ConPrompt compared to other pre-trained models. Additionally, we empirically show that ConPrompt is effective in mitigating identity term bias, demonstrating that it not only makes a model more generalizable but also reduces unintended bias. We analyze the representation quality of ToxiGen-ConPrompt and show its ability to consider target group and toxicity, which are desirable features in terms of implicit hate speeches.",
+}