# Value Lens: Using Large Language Models to Understand Human Values

arXiv: https://arxiv.org/abs/2512.15722 · HTML: https://mlsanity.com/paper/2512.15722/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 4 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [Values in the Wild: Discovering and Analyzing Values in Real-World Language Model Interactions](https://mlsanity.com/paper/2504.15236/) (2025)
- [Strong and weak alignment of large language models with human values](https://mlsanity.com/paper/2408.04655/) (2024)
- [Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models](https://mlsanity.com/paper/2305.08625/) (2023)
- [EAVIT: Efficient and Accurate Human Value Identification from Text data via LLMs](https://mlsanity.com/paper/2505.12792/) (2025)
- [Epicurus at SemEval-2023 Task 4: Improving Prediction of Human Values behind Arguments by Leveraging Their Definitions](https://mlsanity.com/paper/2302.13925/) (2023)
- [Towards a Human Values Dashboard for Software Development: An Exploratory Study](https://mlsanity.com/paper/2107.11273/) (2021)
- [Rudolf Christoph Eucken at SemEval-2023 Task 4: An Ensemble Approach for Identifying Human Values from Arguments](https://mlsanity.com/paper/2305.05335/) (2023)
- [Mao-Zedong At SemEval-2023 Task 4: Label Represention Multi-Head Attention Model With Contrastive Learning-Enhanced Nearest Neighbor Mechanism For Multi-Label Text Classification](https://mlsanity.com/paper/2307.05174/) (2023)

## Built on by (most cited first, 3 of 3)

- [Measuring Human Value Expression in Social Media Texts: Calibrated LLM Annotation and Encoder Transfer](https://mlsanity.com/paper/2606.11018/) (2026)
- [KVpop -- Key-Value Cache Compression with Predictive Online Pruning](https://mlsanity.com/paper/2607.05061/) (2026)
- [Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture](https://mlsanity.com/paper/2605.27373/) (2026)

## Abstract (arXiv)

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.