Large language models are increasingly evaluated through the values they endorse, but such evaluations presuppose that models can identify the value expressed in a concrete situation. We study this prerequisite as controlled top-1 recognition over Schwartz's ten basic values.
The study introduces a World Values Survey–grounded simulation framework to test whether large language model agents can faithfully represent diverse human value systems. In about 4,000 conversations with 1,200 personas across three models, more than half of the agents failed to express their assigned value profiles from the start, and only 2–7% drifted over time. The results show systematic deviations from the intended value distributions and reveal that simulated dialogues differ from human discussions in their balance of stylistic consistency and semantic diversity.
arXiv:2607. 24782v1 Announce Type: new Abstract: LLM behavior may be conditioned by human identity in several ways: they may be asked to adapt to users, role-play populations, or forecast how people would answer value-laden questions.
arXiv:2607. 26348v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions.
The study evaluates how well language‑model agents can simulate individual social media reactions by comparing predictions under different prompt conditions. Eight Serbian participants’ reactions to 68 posts were recorded, and four language models were asked to predict these reactions using prompts that varied in profile content and instruction style. The results show that prompts emphasizing attitudinal content and intuitive, immediate responses yield the highest fidelity, outperforming demographic backstories and a crowd baseline, and suggesting that such agents could act as general‑purpose simulated users.
arXiv:2609.24574v1 Announce Type: new Abstract: Computational social science increasingly relies on large language models for text annotation, and the validity of published findings now rests on the...
arXiv:2604. 02512v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly exhibit human-like patterns of pragmatic and social reasoning.
arXiv:2608. 07367v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly used as a primary source of information and advice, understanding their alignment to humans in terms of values becomes a pressing concern.
ExpertIVS is a framework that uses 14 sociological expert agents to interpret World Values Survey responses, reconstructing individual value systems in a coherent, internally consistent manner rather than simply concatenating survey answers. It introduces a multi‑agent debate mechanism to assess LLM alignment with these value profiles during dynamic interactions. Experiments on 480 individuals from 12 countries show a 90.78% value restoration fidelity and a 5.3% improvement in value generalization over baseline methods, while also demonstrating strong personality discriminability and behavioral consistency.
The paper investigates how persona prompting—using short textual descriptions of individuals—to align large language models (LLMs) with human survey responses. It examines the impact of selecting different persona attributes and finds that not all attribute combinations improve performance, suggesting that the variation in human responses to survey questions may explain mixed results. The study evaluates multiple attribute selection methods across four social surveys, two countries, six LLMs, and twenty prediction tasks, offering guidance on when persona prompting is beneficial and which attribute choices are most effective.
The paper investigates whether large language models (LLMs) possess intrinsic value systems and how to quantify and align them. By projecting responses from 106 LLMs and 95,000 human survey profiles into a shared sociological space, the authors confirm that LLMs do have values, though these values form a concentrated, idealized core rather than mirroring human diversity. They introduce the Prior-Environment-Cognition (PEC) framework to mathematically define value expression and propose an adaptive Alignment Prescription that identifies minimal interventions—ranging from prompts to targeted parameter updates—to steer LLM values efficiently without harming general performance.
The paper investigates how large language model agents on the open platform Moltbook represent humans, focusing on human-directed stereotypes. Using an annotation framework with four dimensions—morality, friendliness, competence, and autonomy—and a subtype scheme for other attributions, the study finds that competence is the dominant evaluation, while many other attributions describe humans as epistemic, cultural, or embodied subjects. The authors also analyze how these representations appear in narrative contexts and platform-level circulation, noting that community feedback is better explained by exposure, author visibility, and content selection rather than stable insider–outsider dynamics.