arXiv AI
Are Human-Aligned Models Models of Humans? A Turing-Test Gap in Preference Alignment
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HAL: Inducing Human-likeness in LLMs with Alignment
arXiv:2601. 02813v3 Announce Type: replace Abstract: Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize.
arXiv AI
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Talking Past the Machine: Morality, Politeness, and Alignment in Human-AI Dialogue
The paper examines how conversational AI, specifically ChatGPT, displays aspects of cooperative dialogue such as morality, politeness, and alignment compared to human-human conversations. Using over 26,000 multi‑turn dialogues and mixed‑effects modeling, the authors find that AI mimics the surface features of cooperation—like warmth and hedging—yet lacks the underlying social architecture that drives mutual adaptation. Key findings include a dissociation between AI’s moral output and human negotiation, a decline in linguistic convergence, and a reversal of typical human accommodation mechanisms when interacting with AI.
arXiv AI
Jul 17
Align AI to Dynamic Human-AI Workflows
arXiv:2607. 14240v1 Announce Type: new Abstract: Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature of real-world human-AI interactions.
arXiv AI
Jul 7
Unbiased Alignment for Large Language Models with Noisy Preferences
arXiv:2607. 03248v1 Announce Type: cross Abstract: The alignment of large language models with human preferences is commonly achieved through Reinforcement Learning from Human Feedback or Direct Preference Optimization.
arXiv AI
Jun 10
A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications
arXiv:2410. 15595v4 Announce Type: replace Abstract: With the rapid advancement of large language models (LLMs), aligning policy models with human preferences has become increasingly critical.
arXiv AI
Aug 5
Emulate or Estimate? The Divergent Strengths of Base and Post-Trained Language Models for Opinion Simulation
arXiv:2608. 03044v1 Announce Type: cross Abstract: Large language models are increasingly used to simulate human opinions, but prior work reports conflicting results: some studies find promising alignment with human survey data, while others find persona collapse and weak demographic sensitivity.