Distilling Comprehensive Cultural Commonsense Knowledge from Large Language Models

Core Concepts

A methodology called MANGO for efficiently distilling high-quality, high-coverage cultural commonsense knowledge (CCSK) assertions from large language models (LLMs).

Abstract

The paper presents the MANGO workflow for distilling cultural commonsense knowledge (CCSK) from large language models (LLMs). The key steps are:

Assertion Generation:

Generating CCSK assertions for given concepts and cultures separately using prompts on GPT-3.5. This allows the model to decide relevant concept-culture combinations.
Iteratively generating new concepts and cultures based on the outputs of the first run.

Assertion Consolidation:

Clustering similar CCSK assertions based on concepts and cultures to reduce redundancy and obtain frequency signals.
Generating a representative summary sentence for each cluster.
The MANGO workflow, when executed with GPT-3.5, yields 167K high-quality CCSK assertions covering 30K concepts and 11K cultures, substantially surpassing prior resources.
An extrinsic evaluation on intercultural dialogue generation tasks shows that augmenting LLM prompts with MANGO assertions significantly improves the specificity, cultural sensitivity and overall quality of the generated responses, as judged by human annotators.

Stats

Tipping is not a common practice in Japan and can be considered rude or impolite.
Shaking hands when greeting is a common practice in Western countries, but not in Southeast Asia where people wai each other.
Teenagers have a different way of greeting compared to adults in many cultures.

Quotes

"To adapt AI applications to specific user contexts, the goal of this research is to capture culturally aware commonsense knowledge, CCSK for short."
"The twofold challenge is to expand the coverage of cultural groups and culture-specific assertions, while maintaining or even improving the quality of the assertions."

Key Insights Distilled From

Multi-Cultural Commonsense Knowledge Distillation

by Tuan-Phong N... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2402.10689.pdf

Deeper Inquiries

How can the MANGO methodology be extended to capture more nuanced cultural differences, such as within-culture variations based on demographic factors like age, gender, socioeconomic status, etc.?

To capture more nuanced cultural differences within a culture, the MANGO methodology can be extended in the following ways:

Fine-grained Cultural Grouping: Instead of treating cultures as monolithic entities, MANGO can create subgroups within a culture based on demographic factors like age, gender, income level, education, etc. This would allow for more specific and targeted assertions that cater to the diverse characteristics within a culture.

Customized Prompts: By tailoring prompts to include specific demographic information, such as "teenagers in Japan" or "elderly women in India," the generated assertions can reflect the nuances of different demographic groups within a culture.

Incorporating Intersectionality: Consider how different demographic factors intersect to shape cultural beliefs and practices. For example, exploring how gender norms vary among different age groups within a culture or how socioeconomic status influences cultural behaviors.

Human-in-the-loop Validation: Engage experts or community members from diverse demographic backgrounds to validate the generated assertions and provide feedback on their accuracy and relevance. This iterative process can help refine the CCSK to better capture within-culture variations.

Data Augmentation: Incorporate additional sources of data, such as surveys, interviews, or ethnographic studies, to supplement the knowledge distilled by LLMs. This can provide a more comprehensive understanding of cultural nuances and variations.

By implementing these strategies, MANGO can enhance its ability to capture the intricate and multifaceted nature of cultural differences within a society, taking into account demographic factors that influence beliefs and behaviors.

How can the transparency and interpretability of the CCSK assertions be further improved to enable better human understanding and trust in the underlying knowledge?

Transparency and interpretability are crucial for ensuring the credibility and usability of CCSK assertions. Here are some ways to enhance these aspects:

Explanation Generation: Develop a mechanism to provide explanations for how each assertion was generated by the LLM. This could involve highlighting the key words or phrases in the prompt that led to the specific assertion, making the reasoning process more transparent.

Interactive Visualization: Create interactive tools or dashboards that allow users to explore the CCSK assertions, view clusters, and understand the relationships between concepts and cultures. Visual representations can aid in comprehension and foster trust in the knowledge base.

Metadata Annotation: Include metadata tags with each assertion, indicating the source of the information, the confidence level, and any relevant contextual details. This metadata can help users assess the reliability and relevance of the assertions.

User Feedback Mechanism: Implement a feedback loop where users can provide input on the accuracy and cultural sensitivity of the assertions. This continuous feedback can help refine the knowledge base and improve its quality over time.

Documentation and Guidelines: Provide detailed documentation on the methodology used for assertion generation, the criteria for inclusion in the knowledge base, and guidelines for interpreting the assertions. Clear guidelines can assist users in understanding the context and limitations of the CCSK.

By incorporating these strategies, MANGO can enhance the transparency and interpretability of its CCSK assertions, empowering users to engage with the knowledge base more effectively and build trust in the information provided.

What are the potential risks and ethical considerations in deploying CCSK in real-world AI systems, and how can they be mitigated?

Deploying CCSK in real-world AI systems presents several risks and ethical considerations that need to be addressed to ensure responsible and equitable use. Here are some key considerations and mitigation strategies:

Bias and Stereotyping: CCSK assertions may inadvertently reinforce cultural biases or stereotypes. Mitigation involves regular auditing of the knowledge base for bias, diverse representation in data sources, and the inclusion of counter-narratives to provide a balanced view.

Privacy and Consent: Collecting cultural knowledge may involve sensitive information about individuals or communities. Ensuring data anonymization, obtaining consent for data usage, and adhering to data protection regulations are essential for safeguarding privacy.

Algorithmic Fairness: AI systems using CCSK should be designed to prioritize fairness and avoid discrimination. Employing fairness metrics, bias detection tools, and diverse training data can help mitigate algorithmic biases.

Transparency and Accountability: Users should be informed about the sources of CCSK, the methodology used for assertion generation, and the limitations of the knowledge base. Establishing clear accountability mechanisms and channels for redressal can enhance transparency.

Cultural Sensitivity: CCSK should be handled with cultural sensitivity and respect for diverse beliefs and practices. Cultural experts, community consultations, and cultural validation processes can ensure that the knowledge base is culturally appropriate and accurate.

Continual Monitoring and Evaluation: Regular monitoring of AI systems using CCSK is essential to detect and address any emerging ethical issues or biases. Conducting impact assessments and soliciting feedback from diverse stakeholders can help in ongoing evaluation and improvement.

By proactively addressing these risks and ethical considerations, organizations can deploy CCSK in AI systems responsibly, promoting cultural understanding, diversity, and inclusivity in the digital landscape.