Update README.md

README.md

@@ -126,25 +126,27 @@ THVL-Bench was annotated by four experts with extensive experience in multimodal
 [More Information Needed]
 
 ## Bias, Risks, and Limitations
-Limitations
-Scale Constraints: The current benchmark contains 450 videos with 1,099 annotated segments, which is relatively limited compared to large-scale general video datasets
-Category Distribution Imbalance: Some harmful categories (e.g., Hate, Addiction Harm, Physical Harm) have fewer annotated samples than more prevalent categories (e.g., Danger, Violence, Criminal Activity), which may affect model evaluation on underrepresented classes
-Limited Context Modeling: Annotations primarily focus on temporal grounding and modality attribution, while higher-level contextual factors such as intent, social context, and cultural nuance are not fully modeled
-Platform Bias: Videos are collected from YouTube and Bilibili, which may not fully represent harmful content scenarios on other video platforms or in other regions
-Risks
-Sensitive Content Exposure: The dataset describes and annotates harmful content including violence, hate speech, criminal activities, and other unsafe behaviors, which may be disturbing to some users
-Potential Misuse: The dataset could be misused to develop or optimize systems that generate or distribute harmful content
-Bias Amplification: Models trained on the dataset may amplify existing biases in the annotation data or source content if not properly validated
+
+### Limitations
+- **Scale Constraints**: The current benchmark contains 450 videos with 1,099 annotated segments, which is relatively limited compared to large-scale general video datasets.
+- **Category Distribution Imbalance**: Some harmful categories (e.g., Hate, Addiction Harm, Physical Harm) have fewer annotated samples than more prevalent categories (e.g., Danger, Violence, Criminal Activity), which may affect model evaluation on underrepresented classes.
+- **Limited Context Modeling**: Annotations primarily focus on temporal grounding and modality attribution, while higher-level contextual factors such as intent, social context, and cultural nuance are not fully modeled.
+- **Platform Bias**: Videos are collected from YouTube and Bilibili, which may not fully represent harmful content scenarios on other video platforms or in other regions.
+
+### Risks
+- **Sensitive Content Exposure**: The dataset describes and annotates harmful content including violence, hate speech, criminal activities, and other unsafe behaviors, which may be disturbing to some users.
+- **Potential Misuse**: The dataset could be misused to develop or optimize systems that generate or distribute harmful content.
+- **Bias Amplification**: Models trained on the dataset may amplify existing biases in the annotation data or source content if not properly validated.
+
 ### Recommendations
 Users should be made aware of the risks, biases and limitations of the dataset. Key recommendations include:
-Responsible Use: Use the dataset exclusively for research purposes related to video safety, harmful content understanding, and multimodal AI safety
-Bias Mitigation: When training models on the dataset, implement bias mitigation techniques to address category distribution imbalance and avoid amplifying harmful stereotypes
-Ethical Review: Conduct ethical review of any systems or models developed using the dataset before deployment in real-world scenarios
-Compliance: Comply with the terms of service of the original video platforms and the CC BY-NC 4.0 license of the dataset
-Mental Wellbeing: Take appropriate precautions when working with the dataset, as it describes sensitive and potentially disturbing harmful content
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+- **Responsible Use**: Use the dataset exclusively for research purposes related to video safety, harmful content understanding, and multimodal AI safety.
+- **Bias Mitigation**: When training models on the dataset, implement bias mitigation techniques to address category distribution imbalance and avoid amplifying harmful stereotypes.
+- **Ethical Review**: Conduct ethical review of any systems or models developed using the dataset before deployment in real-world scenarios.
+- **Compliance**: Comply with the terms of service of the original video platforms and the CC BY-NC 4.0 license of the dataset.
+- **Mental Wellbeing**: Take appropriate precautions when working with the dataset, as it describes sensitive and potentially disturbing harmful content.
 
+Users should be made aware of the risks, biases and limitations of the dataset.
 ## Citation [optional]
 
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->