A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challenges
- URL: http://arxiv.org/abs/2409.13521v1
- Date: Fri, 20 Sep 2024 14:03:06 GMT
- Title: A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challenges
- Authors: Lorenzo Zangari, Candida M. Greco, Davide Picca, Andrea Tagarelli,
- Abstract summary: Moral values have deep roots in early civilizations, codified within norms and laws that regulated societal order and the common good.
The Moral Foundation Theory (MFT) is a well-established framework that identifies the core moral foundations underlying the manner in which different cultures shape individual and social lives.
Recent advancements in natural language processing, particularly Pre-trained Language Models (PLMs), have enabled the extraction and analysis of moral dimensions from textual data.
- Score: 2.435021773579434
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Moral values have deep roots in early civilizations, codified within norms and laws that regulated societal order and the common good. They play a crucial role in understanding the psychological basis of human behavior and cultural orientation. The Moral Foundation Theory (MFT) is a well-established framework that identifies the core moral foundations underlying the manner in which different cultures shape individual and social lives. Recent advancements in natural language processing, particularly Pre-trained Language Models (PLMs), have enabled the extraction and analysis of moral dimensions from textual data. This survey presents a comprehensive review of MFT-informed PLMs, providing an analysis of moral tendencies in PLMs and their application in the context of the MFT. We also review relevant datasets and lexicons and discuss trends, limitations, and future directions. By providing a structured overview of the intersection between PLMs and MFT, this work bridges moral psychology insights within the realm of PLMs, paving the way for further research and development in creating morally aware AI systems.
Related papers
- Are Rules Meant to be Broken? Understanding Multilingual Moral Reasoning as a Computational Pipeline with UniMoral [17.46198411148926]
Moral reasoning is a complex cognitive process shaped by individual experiences and cultural contexts.
We bridge this gap with UniMoral, a unified dataset integrating psychologically grounded and social-media-derived moral dilemmas.
We demonstrate UniMoral's utility through a benchmark evaluations of three large language models (LLMs) across four tasks.
arXiv Detail & Related papers (2025-02-19T20:13:24Z) - M$^3$oralBench: A MultiModal Moral Benchmark for LVLMs [66.78407469042642]
We introduce M$3$oralBench, the first MultiModal Moral Benchmark for LVLMs.
M$3$oralBench expands the everyday moral scenarios in Moral Foundations Vignettes (MFVs) and employs the text-to-image diffusion model, SD3.0, to create corresponding scenario images.
It conducts moral evaluation across six moral foundations of Moral Foundations Theory (MFT) and encompasses tasks in moral judgement, moral classification, and moral response.
arXiv Detail & Related papers (2024-12-30T05:18:55Z) - Large Language Models Reflect the Ideology of their Creators [71.65505524599888]
Large language models (LLMs) are trained on vast amounts of data to generate natural language.
This paper shows that the ideological stance of an LLM appears to reflect the worldview of its creators.
arXiv Detail & Related papers (2024-10-24T04:02:30Z) - MoralBench: Moral Evaluation of LLMs [34.43699121838648]
This paper introduces a novel benchmark designed to measure and compare the moral reasoning capabilities of large language models (LLMs)
We present the first comprehensive dataset specifically curated to probe the moral dimensions of LLM outputs.
Our methodology involves a multi-faceted approach, combining quantitative analysis with qualitative insights from ethics scholars to ensure a thorough evaluation of model performance.
arXiv Detail & Related papers (2024-06-06T18:15:01Z) - Exploring and steering the moral compass of Large Language Models [55.2480439325792]
Large Language Models (LLMs) have become central to advancing automation and decision-making across various sectors.
This study proposes a comprehensive comparative analysis of the most advanced LLMs to assess their moral profiles.
arXiv Detail & Related papers (2024-05-27T16:49:22Z) - Moral Foundations of Large Language Models [6.6445242437134455]
Moral foundations theory (MFT) is a psychological assessment tool that decomposes human moral reasoning into five factors.
As large language models (LLMs) are trained on datasets collected from the internet, they may reflect the biases that are present in such corpora.
This paper uses MFT as a lens to analyze whether popular LLMs have acquired a bias towards a particular set of moral values.
arXiv Detail & Related papers (2023-10-23T20:05:37Z) - Enhancing Stance Classification on Social Media Using Quantified Moral Foundations [7.061680079778037]
We investigate how moral foundation dimensions can contribute to predicting an individual's stance on a given target.
We incorporate moral foundation features extracted from text, along with message semantic features, to classify stances at both message- and user-levels.
Preliminary results suggest that encoding moral foundations can enhance the performance of stance detection tasks.
arXiv Detail & Related papers (2023-10-15T14:40:57Z) - Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories? [78.3738172874685]
Making moral judgments is an essential step toward developing ethical AI systems.
Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality.
This work proposes a flexible top-down framework to steer (Large) Language Models (LMs) to perform moral reasoning with well-established moral theories from interdisciplinary research.
arXiv Detail & Related papers (2023-08-29T15:57:32Z) - Aligning AI With Shared Human Values [85.2824609130584]
We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality.
We find that current language models have a promising but incomplete ability to predict basic human ethical judgements.
Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.
arXiv Detail & Related papers (2020-08-05T17:59:16Z) - On the Morality of Artificial Intelligence [154.69452301122175]
We propose conceptual and practical principles and guidelines for Machine Learning research and deployment.
We insist on concrete actions that can be taken by practitioners to pursue a more ethical and moral practice of ML aimed at using AI for social good.
arXiv Detail & Related papers (2019-12-26T23:06:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.