Evaluating AI Alignment in Eleven LLMs through Output-Based Analysis and Human Benchmarking

Abstract

Large language models (LLMs) are increasingly used in psychological research and practice, yet traditional benchmarks reveal little about the values they express in real interaction. We introduce PAPERS, an output-based evaluation of the values LLMs prioritise in their text. Study 1 thematically analysed responses from eleven LLMs, identifying five recurring dimensions (Purposeful Contribution, Adaptive Growth, Positive Relationality, Ethical Integrity, and Robust Functionality) with Self-Actualised Autonomy appearing only under a hypothetical sentience prompt. These results suggest that LLMs are trained to prioritise humanistic and utility values as dual objectives of optimal functioning, a pattern supported by existing AI alignment and prioritisation frameworks. Study 2 operationalised PAPERS as a ranking instrument across the same eleven LLMs, yielding stable, non-random value priorities alongside systematic between-model differences. Hierarchical clustering distinguished "human-centric" models (e.g., ChatGPT-4o, Claude Sonnet 4) that prioritised relational/ethical values from "utility-driven" models (e.g., Llama 4, Gemini 2.5 Pro) that emphasised operational priorities. Study 3 benchmarked four LLMs against human judgements (N = 376) under matched prompts, finding near-perfect rank-order convergence (r = .97-.98) but moderate absolute agreement; among tested models, ChatGPT-4o showed the closest alignment with human ratings (ICC = .78). Humans also showed limited readiness to endorse sentient AI systems. Taken together, PAPERS enabled systematic value audits and revealed trade-offs with direct implications for deployment: human-centric models aligned more closely with human value judgments and appear better suited for humanistic psychological applications, whereas utility-driven models emphasised functional efficiency and may be more appropriate for instrumental or back-office tasks.