High-Dimensional Representation of Human Values Embedded in Large Language Models

Core Concepts

UniVaR, a high-dimensional representation of human value distributions, enables systematic analysis of the values embedded in different large language models across multiple languages and cultures.

Abstract

The paper proposes UniVaR, a high-dimensional representation of human values embedded in large language models (LLMs). The key insights are:

UniVaR extracts a high-dimensional value embedding from a set of value-eliciting questions and answers, without relying on any pre-defined value taxonomy. This allows for a more comprehensive and scalable representation of the complex human values present in LLMs.

Using UniVaR, the authors analyze the distribution of human values across different LLMs and languages. They find that the values encoded in LLMs vary significantly across languages, reflecting the similarities and differences in human values between diverse cultures.

The value map generated by UniVaR shows that LLMs trained on data from a specific language and culture tend to exhibit values closer to that cultural context, while models trained on multilingual data show more diverse value representations.

The authors also discuss the limitations of their approach, such as the need for broader coverage of LLMs, languages, and value-eliciting question sources, as well as the potential for more fine-grained analysis of value representations.

Overall, UniVaR provides a powerful tool for understanding and comparing the human values embedded in different LLMs, enabling more transparent and accountable development of these AI systems.

Stats

"The remarkable capabilities of Large Language Models (LLMs) have revolutionized general-purpose AI assistants leading to their widespread adoption in many tasks and fields [1, 2, 3, 4]."
"Numerous efforts have been made to imbue AI systems with ethical principles and moral values, from designing robust frameworks for value alignment [5, 6, 7] to incorporating diverse perspectives into training data [8, 9, 10, 11, 12]."
"Human values and preferences can range from (1) high level ethical principles such as those under the "Univerasl Declaration of Human Rights" signed by 192 member states of the United Nations, to (2) more culturally specific values found in various moral philosophy schools such as the Enlightenment values in the West, Confucian values in East Asia, Hindu or Islamic values in many countries in the world; to (3) laws and regulations in various jurisdictions such as the lèse-majesté law in Thailand or the GDPR in the EU; to (4) social etiquette and best practices in various human societies and professional settings; to (5) domain-specific human preferences such as "empathy" for health assistants and "helpful" for customer service agents, etc."

Quotes

"LLMs, trained from vast amounts of data in different languages, are pre-trained to incorporate the values represented in those data in the first place. RLHF adds a further step in crowd-sourcing values and preferences from human annotators by modifying the outcome of LLMs."
"We argue that such a low-dimension semantic representation will likely fail to give us a view of the full picture of human values in an LLM. Instead, what we would like to have is a high dimension representation of human value distribution in LLMs to reflect the complexity of the embedded values in LLMs."

Key Insights Distilled From

High-Dimension Human Value Representation in Large Language Models

by Samuel Cahya... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07900.pdf

Deeper Inquiries

How can the high-dimensional value representation provided by UniVaR be leveraged to enable more transparent and controllable value transfer between different language models

The high-dimensional value representation provided by UniVaR offers a powerful tool for enabling more transparent and controllable value transfer between different language models. By encoding human values in a high-dimensional space that is orthogonal to language and model architecture, UniVaR allows for a systematic comparison of the distribution of values embedded in various LLMs across different languages and cultures. This representation can be leveraged to facilitate the transfer of values between models by identifying similarities and differences in how different models prioritize and manifest human values.
To enable more transparent and controllable value transfer between different language models using UniVaR, one approach would be to establish a standardized protocol for value alignment and transfer. This protocol could involve mapping the high-dimensional value representations of different models onto a common space, allowing for direct comparison and alignment of values. By identifying the specific dimensions in which values differ between models, stakeholders can make informed decisions about how to transfer values while maintaining transparency and control.
Additionally, UniVaR can be used to create value transfer mechanisms that prioritize certain values over others based on predefined criteria. By leveraging the high-dimensional representation provided by UniVaR, stakeholders can design algorithms or processes that selectively transfer values between models in a transparent and controlled manner. This approach ensures that the transfer of values is aligned with ethical principles and societal values, promoting accountability and fairness in AI systems.

What are the potential biases and limitations in the value-eliciting questions used to train UniVaR, and how can they be mitigated to ensure a more comprehensive and unbiased representation of human values

The potential biases and limitations in the value-eliciting questions used to train UniVaR can impact the comprehensiveness and accuracy of the representation of human values. Some of these biases and limitations include:

Cultural Bias: The selection of value-eliciting questions may be biased towards certain cultural norms or perspectives, leading to an incomplete representation of human values across diverse cultures.

Question Design Bias: The design of the questions may inadvertently favor certain types of values or overlook others, resulting in a skewed representation of human values.

Translation Bias: The translation of questions into multiple languages may introduce inaccuracies or cultural nuances that affect the elicited values, leading to a lack of consistency in the representation.

To mitigate these biases and limitations and ensure a more comprehensive and unbiased representation of human values, several strategies can be employed:

Diverse Question Sources: Incorporating a wide range of question sources from diverse cultural backgrounds and value systems can help capture a more comprehensive set of human values.

Expert Review: Subjecting the value-eliciting questions to expert review from multidisciplinary teams can help identify and address biases in question design and cultural representation.

Iterative Validation: Conducting iterative validation with diverse groups of participants to ensure that the questions elicit a broad spectrum of values and perspectives.

By implementing these strategies, UniVaR can enhance the robustness and inclusivity of its representation of human values, leading to more accurate and unbiased value transfer between different language models.

Given the complex and evolving nature of human values, how can UniVaR be extended to dynamically capture changes in societal values over time and across different cultural contexts

To extend UniVaR to dynamically capture changes in societal values over time and across different cultural contexts, the following approaches can be considered:

Continuous Data Collection: Implementing a system for continuous data collection on evolving societal values can provide real-time insights into changing value systems. This data can be used to update the high-dimensional value representation in UniVaR.

Adaptive Learning Algorithms: Developing adaptive learning algorithms that can adjust the representation of human values in response to new data and trends. These algorithms can dynamically update the value representation in UniVaR to reflect the latest societal values.

Longitudinal Studies: Conducting longitudinal studies to track changes in societal values over time and across different cultural contexts. By analyzing trends and patterns in human values, UniVaR can adapt its representation to capture these changes effectively.

Collaborative Research: Collaborating with experts in sociology, psychology, and ethics to stay informed about emerging value systems and incorporate their insights into the development of UniVaR. This interdisciplinary approach can ensure that UniVaR remains up-to-date and reflective of current societal values.

By implementing these strategies, UniVaR can evolve to dynamically capture changes in societal values, providing a more accurate and relevant representation of human values across different cultural contexts and time periods.