Value Lens: Using Large Language Models to Understand Human Values

TL;DR

Value Lens introduces a two-stage, LLM-driven framework to detect and quantify human values in text for AI decision alignment. It first conceptualises a value theory with expert refinement, then uses two LLMs (detection and intensity critique) to identify values and their strength within passages. Compared with other approaches, it delivers competitive macro F1 and notable value-intensity outputs, supporting more nuanced value-aligned decisions. The method avoids training requirements and emphasizes continuous expert refinement, offering practical impact for value-aware autonomous systems.

Abstract

The autonomous decision-making process, which is increasingly applied to computer systems, requires that the choices made by these systems align with human values. In this context, systems must assess how well their decisions reflect human values. To achieve this, it is essential to identify whether each available action promotes or undermines these values. This article presents Value Lens, a text-based model designed to detect human values using generative artificial intelligence, specifically Large Language Models (LLMs). The proposed model operates in two stages: the first aims to formulate a formal theory of values, while the second focuses on identifying these values within a given text. In the first stage, an LLM generates a description based on the established theory of values, which experts then verify. In the second stage, a pair of LLMs is employed: one LLM detects the presence of values, and the second acts as a critic and reviewer of the detection process. The results indicate that Value Lens performs comparably to, and even exceeds, the effectiveness of other models that apply different methods for similar tasks.

Figures (2)

• Figure 1: Stage 1: value theory conceptualisation.
• Figure 2: Stage 2: value detection.