High-Dimension Human Value Representation in Large Language Models

Samuel Cahyawijaya, Delong Chen, Yejin Bang, Leila Khalatbari, Bryan Wilie, Ziwei Ji, Etsuko Ishii, Pascale Fung

Keywords: Value Embedding, Human Value in LLM, LLM Value Alignment, Cultural Understanding in LLM

TL;DR: We propose UniVaR, a scalable self-supervised high-dimensional neural representation of symbolic human value distributions in LLMs.

Abstract:

The widespread application of Large Language Models (LLMs) across various tasks and fields has necessitated the alignment of these models with human values and preferences. Given various approaches of human value alignment, there is an urgent need to understand the scope and nature of human values injected into these LLMs before their deployment and adoption. We propose UniVaR, a high-dimensional neural representation of symbolic human value distributions in LLMs, orthogonal to model architecture and training data. This is a continuous and scalable representation, self-supervised from the value-relevant output of 8 LLMs and evaluated on 15 open-source and commercial LLMs. Through UniVaR, we visualize and explore how LLMs prioritize different values in 25 languages and cultures, shedding light on the complex interplay between human values and language modeling.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 4687