arXiv Machine Learning
Alignment Myopia in Generative AI
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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.
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Constructive Alignment: Governing Preference Dynamics in Human-AI Interaction
arXiv:2607. 00001v1 Announce Type: new Abstract: Most approaches to AI alignment treat human preferences as fixed targets to be inferred and optimized.
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Align AI to Dynamic Human-AI Workflows
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Model vs. User: Whose Values Prevail? Inference-Time Value Alignment in Multi-Turn Conversations
arXiv:2610.19817v1 Announce Type: new Abstract: Human values shape how we expect language models to behave. Dominant alignment approaches treat values as global objectives, although the appropriate v...
arXiv AI
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TUX: Measuring Human--AI Tacit Understanding
The paper introduces TUX, a Tacit Understanding Index that measures how similarly humans and large language models (LLMs) place concepts along subjective spectra in a task inspired by the game Wavelength. Using 241 human participants and 200 profile-conditioned LLM agents across four models, the study finds that human–agent pairs with similar traits achieve higher TUX scores, indicating that tacit alignment is linked to person-level characteristics. Regression analyses show that richer predictor sets—including individual traits, decision-making styles, and confidence—improve the explainability of TUX beyond simple trait-distance baselines.
arXiv AI
Sep 21
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.