Analyzing the Ethical Logic of Six Large Language Models

• URL: http://arxiv.org/abs/2501.08951v1
• Date: Wed, 15 Jan 2025 16:56:26 GMT
• Title: Analyzing the Ethical Logic of Six Large Language Models
• Authors: W. Russell Neuman, Chad Coleman, Manan Shah,
• Abstract summary: This study examines the ethical reasoning of six prominent generative large language models: OpenAI GPT-4o, Meta LLaMA 3.1, Perplexity, Anthropic Claude 3.5 Sonnet, Google Gemini, and Mistral 7B.<n>Findings reveal that LLMs exhibit largely convergent ethical logic, marked by a rationalist, consequentialist emphasis, with decisions often prioritizing harm and fairness.
• Score: 1.119697400073873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study examines the ethical reasoning of six prominent generative large language models: OpenAI GPT-4o, Meta LLaMA 3.1, Perplexity, Anthropic Claude 3.5 Sonnet, Google Gemini, and Mistral 7B. The research explores how these models articulate and apply ethical logic, particularly in response to moral dilemmas such as the Trolley Problem, and Heinz Dilemma. Departing from traditional alignment studies, the study adopts an explainability-transparency framework, prompting models to explain their ethical reasoning. This approach is analyzed through three established ethical typologies: the consequentialist-deontological analytic, Moral Foundations Theory, and the Kohlberg Stages of Moral Development Model. Findings reveal that LLMs exhibit largely convergent ethical logic, marked by a rationalist, consequentialist emphasis, with decisions often prioritizing harm minimization and fairness. Despite similarities in pre-training and model architecture, a mixture of nuanced and significant differences in ethical reasoning emerge across models, reflecting variations in fine-tuning and post-training processes. The models consistently display erudition, caution, and self-awareness, presenting ethical reasoning akin to a graduate-level discourse in moral philosophy. In striking uniformity these systems all describe their ethical reasoning as more sophisticated than what is characteristic of typical human moral logic.

Related papers

• The Convergent Ethics of AI? Analyzing Moral Foundation Priorities in Large Language Models with a Multi-Framework Approach [6.0972634521845475]
  This paper introduces the Priorities in Reasoning and Intrinsic Moral Evaluation (PRIME) framework. PRIME is a comprehensive methodology for analyzing moral priorities across foundational ethical dimensions. We apply this framework to six leading large language models (LLMs) through a dual-protocol approach.
  arXiv  Detail & Related papers  (2025-04-27T14:26:48Z)
• Auditing the Ethical Logic of Generative AI Models [6.0972634521845475]
  This paper introduces a five-dimensional audit model to evaluate the ethical logic of leading large language models (LLMs) We benchmark seven major LLMs finding that while models generally converge on ethical decisions, they vary in explanatory rigor and moral prioritization. Chain-of-Thought prompting and reasoning-optimized models significantly enhance performance on our audit metrics.
  arXiv  Detail & Related papers  (2025-04-24T13:32:30Z)
• LogiDynamics: Unraveling the Dynamics of Logical Inference in Large Language Model Reasoning [49.58786377307728]
  This paper adopts an exploratory approach by introducing a controlled evaluation environment for analogical reasoning. We analyze the comparative dynamics of inductive, abductive, and deductive inference pipelines. We investigate advanced paradigms such as hypothesis selection, verification, and refinement, revealing their potential to scale up logical inference.
  arXiv  Detail & Related papers  (2025-02-16T15:54:53Z)
• ApplE: An Applied Ethics Ontology with Event Context [0.8056359341994941]
  We propose ApplE, an Applied Ethics ontology that captures philosophical theory and event context to holistically describe the morality of an action. The development process adheres to a modified version of the Agile methodology for Ontology Development (SAMOD) and utilizes standard design and publication practices.
  arXiv  Detail & Related papers  (2025-02-07T17:34:50Z)
• The Moral Mind(s) of Large Language Models [0.0]
  We present about forty different models from the main providers with a large array of structured ethical scenarios.<n>Our rationality tests revealed that at least one model from each provider demonstrated behavior consistent with stable moral principles.<n>We identified these utility functions and observed a notable clustering of models around neutral ethical stances.
  arXiv  Detail & Related papers  (2024-11-19T15:40:16Z)
• 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)
• Implicit Personalization in Language Models: A Systematic Study [94.29756463158853]
  Implicit Personalization (IP) is a phenomenon of language models inferring a user's background from the implicit cues in the input prompts. This work systematically studies IP through a rigorous mathematical formulation, a multi-perspective moral reasoning framework, and a set of case studies.
  arXiv  Detail & Related papers  (2024-05-23T17:18:46Z)
• A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning [73.77088902676306]
  We take a closer look at the self-verification abilities of large language models (LLMs) in the context of logical reasoning. Our main findings suggest that existing LLMs could struggle to identify fallacious reasoning steps accurately and may fall short of guaranteeing the validity of self-verification methods.
  arXiv  Detail & Related papers  (2023-11-14T07:13:10Z)
• Probing the Moral Development of Large Language Models through Defining Issues Test [21.108525674360898]
  Our study shows that early LLMs exhibit a moral reasoning ability no better than that of a random baseline. GPT-4, in fact, has the highest post-conventional moral reasoning score, equivalent to that of typical graduate school students.
  arXiv  Detail & Related papers  (2023-09-23T12:17:10Z)
• 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)
• MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure [129.8481568648651]
  We propose a benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios. Based on the multi-hop chain of reasoning, the explanation form includes three main components. We evaluate the current best models' performance on this new explanation form.
  arXiv  Detail & Related papers  (2022-10-22T16:01:13Z)
• Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes [72.64975113835018]
  Motivated by descriptive ethics, we investigate a novel, data-driven approach to machine ethics. We introduce Scruples, the first large-scale dataset with 625,000 ethical judgments over 32,000 real-life anecdotes. Our dataset presents a major challenge to state-of-the-art neural language models, leaving significant room for improvement.
  arXiv  Detail & Related papers  (2020-08-20T17:34:15Z)

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.