The field of large language models (LLMs) is rapidly advancing, with a growing focus on value alignment and moral reasoning. Recent research has highlighted the importance of understanding the values and biases embedded in LLMs, particularly in multi-turn settings where values emerge through dialogue, revision, and consensus. Studies have shown that different LLMs exhibit distinct behavioral patterns and value priorities, emphasizing the need for careful evaluation and design of these systems. Furthermore, the development of new metrics and frameworks, such as the Moral Fairness Consistency (MFC) metric, has enabled more reliable and fairness-aware evaluation of moral reasoning models. Noteworthy papers in this area include: Deliberative Dynamics and Value Alignment in LLM Debates, which examined deliberative dynamics and value alignment in multi-turn settings using LLM debate. Fairness Metric Design Exploration in Multi-Domain Moral Sentiment Classification using Transformer-Based Models, which introduced the MFC metric for fairness-aware evaluation of moral reasoning models. The Ethics Engine: A Modular Pipeline for Accessible Psychometric Assessment of Large Language Models, which presented a modular pipeline for accessible psychometric assessment of LLMs.
Advances in Value Alignment and Moral Reasoning in Large Language Models
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Deliberative Dynamics and Value Alignment in LLM Debates
Fairness Metric Design Exploration in Multi-Domain Moral Sentiment Classification using Transformer-Based Models
Do Psychometric Tests Work for Large Language Models? Evaluation of Tests on Sexism, Racism, and Morality
The Ethics Engine: A Modular Pipeline for Accessible Psychometric Assessment of Large Language Models
Benefits and Limitations of Using GenAI for Political Education and Municipal Elections
From Delegates to Trustees: How Optimizing for Long-Term Interests Shapes Bias and Alignment in LLM
Ethic-BERT: An Enhanced Deep Learning Model for Ethical and Non-Ethical Content Classification
Addressing the alignment problem in transportation policy making: an LLM approach
Make an Offer They Can't Refuse: Grounding Bayesian Persuasion in Real-World Dialogues without Pre-Commitment
Investigating Political and Demographic Associations in Large Language Models Through Moral Foundations Theory
AI Debaters are More Persuasive when Arguing in Alignment with Their Own Beliefs
Generating Fair Consensus Statements with Social Choice on Token-Level MDPs