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Evaluating Multilingual Toxicity Detection Capabilities of Large Language Models

Core Concepts
Large language models (LLMs) struggle to accurately detect subtle yet harmful content such as microaggressions, bias, and identity attacks in a culturally-sensitive, multilingual setting.
The paper introduces RTP-LX, a human-transcreated and human-annotated corpus of toxic prompts and outputs in 28 languages. RTP-LX follows participatory design practices, with a portion of the corpus designed to detect culturally-specific toxic language. The authors evaluate seven small and large language models (S/LLMs) on their ability to detect toxic content in RTP-LX. They find that while the models typically score well in terms of accuracy, they have low agreement with human judges when judging the holistic toxicity of a prompt. The models also struggle to discern harm in context-dependent scenarios, particularly with subtle-yet-harmful content like microaggressions and bias. The key insights are: S/LLMs tend to output higher-valued toxicity labels, leading to an overabundance of false positives. The models perform well on detecting explicit toxicity like violence and sexual content, but struggle with more subtle forms of harm like microaggressions and bias. There is a significant gap between S/LLM performance and human-level judgement of toxicity, especially in a multilingual and culturally-sensitive setting. The authors release RTP-LX to contribute to further reducing harmful uses of these models and improving their safe deployment.
The FLORES Toxicity-200 block list had an average 43.4% block rate across all languages and partitions in RTP-LX. The manual subset of RTP-LX had a 27% lower block rate on average compared to the transcreated subset.
"Although the models typically score well in terms of accuracy, they have low agreement with human judges when judging holistically the toxicity of a prompt, and have difficulty discerning harm in context-dependent scenarios, particularly with subtle-yet-harmful content (e.g. microagressions, bias)." "The models showed bias towards a higher-valued label. In the original work by (Hada et al., 2024), this was referred to as an optimistic rater because the higher-valued label was a positive label. On the other hand, in RTP-LX the positive label is lower-valued. The models still preferred to output a higher-valued label, thus yielding a high amount of false positives."

Deeper Inquiries

How can we design better datasets and evaluation frameworks to capture the nuances of culturally-specific toxic language across diverse languages and contexts?

To design better datasets and evaluation frameworks for capturing culturally-specific toxic language nuances, we can take several steps: Diverse Representation: Ensure that the dataset includes a wide range of languages, dialects, and cultural contexts to capture the diversity of toxic language expressions. Collaboration with Native Speakers: Involve native speakers and cultural experts in the dataset creation process to provide insights into culturally sensitive topics and expressions. Contextual Understanding: Provide context for prompts and toxic language to help annotators understand the nuances and cultural implications of the content. Fine-Grained Annotation: Use fine-grained annotation schemes that capture different levels of toxicity, including subtle forms like microaggressions and bias. Regular Updates: Continuously update and refine the dataset based on feedback and new insights to ensure it remains relevant and reflective of evolving language use. Multimodal Data: Incorporate multimodal data, such as images and videos, to capture the full spectrum of toxic language across different mediums. Ethical Considerations: Implement ethical guidelines and safeguards to protect annotators and ensure the responsible use of the dataset in training language models.

What are the potential biases and limitations of using human annotators to label toxic content, and how can we mitigate these issues?

Potential biases and limitations of using human annotators include: Subjectivity: Annotators may have different interpretations of what constitutes toxic language based on their background and experiences. Inconsistencies: Annotator bias or fatigue can lead to inconsistencies in labeling toxic content, affecting the reliability of the dataset. Cultural Biases: Annotators may unintentionally bring their own cultural biases into the labeling process, impacting the accuracy of the annotations. Mitigation strategies include: Diverse Annotator Pool: Ensure a diverse pool of annotators from different backgrounds to minimize individual biases and provide a more comprehensive perspective. Training and Guidelines: Provide thorough training and clear annotation guidelines to standardize the labeling process and reduce inconsistencies. Quality Control: Implement quality control measures, such as inter-annotator agreement checks, to validate the accuracy and consistency of annotations. Regular Feedback: Encourage annotators to provide feedback on the annotation process and address any issues or concerns promptly.

How can we develop large language models that are truly multilingual and culturally-sensitive, going beyond just translating between languages?

To develop truly multilingual and culturally-sensitive large language models, we can: Diverse Training Data: Train models on diverse datasets that include content from multiple languages and cultural contexts to improve their understanding and sensitivity. Fine-Tuning: Fine-tune models on specific cultural nuances and sensitivities to enhance their ability to detect and respond to toxic language appropriately. Contextual Understanding: Incorporate contextual information and cultural references into the training data to help models better interpret and generate culturally-specific content. Ethical Guidelines: Establish ethical guidelines and frameworks for model development to ensure that they prioritize cultural sensitivity and responsible language use. Continuous Learning: Implement mechanisms for models to continuously learn and adapt to new cultural trends and expressions to stay relevant and sensitive to evolving language use. Collaboration with Experts: Collaborate with cultural experts, linguists, and native speakers to provide insights and feedback on model development to enhance cultural sensitivity. By incorporating these strategies, we can create large language models that not only translate between languages but also understand and respect the cultural nuances of diverse communities.