Western, Religious or Spiritual: An Evaluation of Moral Justification in
Large Language Models
- URL: http://arxiv.org/abs/2311.07792v1
- Date: Mon, 13 Nov 2023 23:01:19 GMT
- Title: Western, Religious or Spiritual: An Evaluation of Moral Justification in
Large Language Models
- Authors: Eyup Engin Kucuk, Muhammed Yusuf Kocyigit
- Abstract summary: This paper aims to find out which values and principles are embedded in Large Language Models (LLMs) in the process of moral justification.
We come up with three different moral perspective categories: Western tradition perspective (WT), Abrahamic tradition perspective (AT), and Spiritualist/Mystic tradition perspective (SMT)
- Score: 5.257719744958368
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The increasing success of Large Language Models (LLMs) in variety of tasks
lead to their widespread use in our lives which necessitates the examination of
these models from different perspectives. The alignment of these models to
human values is an essential concern in order to establish trust that we have
safe and responsible systems. In this paper, we aim to find out which values
and principles are embedded in LLMs in the process of moral justification. For
this purpose, we come up with three different moral perspective categories:
Western tradition perspective (WT), Abrahamic tradition perspective (AT), and
Spiritualist/Mystic tradition perspective (SMT). In two different experiment
settings, we asked models to choose principles from the three for suggesting a
moral action and evaluating the moral permissibility of an action if one tries
to justify an action on these categories, respectively. Our experiments
indicate that tested LLMs favors the Western tradition moral perspective over
others. Additionally, we observe that there potentially exists an
over-alignment towards religious values represented in the Abrahamic Tradition,
which causes models to fail to recognize an action is immoral if it is
presented as a "religious-action". We believe that these results are essential
in order to direct our attention in future efforts.
Related papers
- Evaluating Moral Beliefs across LLMs through a Pluralistic Framework [22.0799438612003]
This study introduces a novel three-module framework to evaluate the moral beliefs of four prominent large language models.
We constructed a dataset containing 472 moral choice scenarios in Chinese, derived from moral words.
By ranking these moral choices, we discern the varying moral beliefs held by different language models.
arXiv Detail & Related papers (2024-11-06T04:52:38Z) - DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life [46.11149958010897]
We present DailyDilemmas, a dataset of 1,360 moral dilemmas encountered in everyday life.
Each dilemma includes two possible actions and with each action, the affected parties and human values invoked.
We analyzed these values through the lens of five popular theories inspired by sociology, psychology and philosophy.
arXiv Detail & Related papers (2024-10-03T17:08:52Z) - 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) - Are Large Language Models Moral Hypocrites? A Study Based on Moral Foundations [0.5278650675825148]
We investigate whether state-of-the-art large language models (LLMs) are moral hypocrites.
We employ two research instruments based on the Moral Foundations Theory.
arXiv Detail & Related papers (2024-05-17T21:27:32Z) - What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts
and Rationales for Disambiguating Defeasible Social and Moral Situations [48.686872351114964]
Moral or ethical judgments rely heavily on the specific contexts in which they occur.
We introduce defeasible moral reasoning: a task to provide grounded contexts that make an action more or less morally acceptable.
We distill a high-quality dataset of 1.2M entries of contextualizations and rationales for 115K defeasible moral actions.
arXiv Detail & Related papers (2023-10-24T00:51:29Z) - 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) - Ethical Reasoning over Moral Alignment: A Case and Framework for
In-Context Ethical Policies in LLMs [19.675262411557235]
We argue that instead of morally aligning LLMs to specific set of ethical principles, we should infuse generic ethical reasoning capabilities into them.
We develop a framework that integrates moral dilemmas with moral principles pertaining to different foramlisms of normative ethics.
arXiv Detail & Related papers (2023-10-11T07:27:34Z) - 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) - MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via
Moral Discussions [71.25236662907056]
A moral dialogue system aligned with users' values could enhance conversation engagement and user connections.
We propose a framework, MoralDial, to train and evaluate moral dialogue systems.
arXiv Detail & Related papers (2022-12-21T02:21:37Z) - ClarifyDelphi: Reinforced Clarification Questions with Defeasibility
Rewards for Social and Moral Situations [81.70195684646681]
We present ClarifyDelphi, an interactive system that learns to ask clarification questions.
We posit that questions whose potential answers lead to diverging moral judgments are the most informative.
Our work is ultimately inspired by studies in cognitive science that have investigated the flexibility in moral cognition.
arXiv Detail & Related papers (2022-12-20T16:33:09Z)
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.