Personalization, Personas, and Forecasting in Value Alignment

• URL: http://arxiv.org/abs/2607.24782v1
• Date: Sun, 21 Jun 2026 01:38:22 GMT
• Title: Personalization, Personas, and Forecasting in Value Alignment
• Abstract summary: We show that prompt framing is not a cosmetic choice in cultural value elicitation.<n>Across 21,008 model-response rows, prompt framing is a first-order of cultural alignment.
• Score: 16.709103453776176
• License: http://creativecommons.org/licenses/by/4.0/
• 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. We test whether these framings are interchangeable using the World Values Survey (WVS). We evaluate GPT-5.4, Claude Sonnet 4.6, Gemini 2.5 Flash, and Qwen3-235B on 101 WVS-derived questions across 13 language-country slices, comparing a language-only baseline with user-country, persona-country, and third-person prompts. Across 21,008 model-response rows, prompt framing is a first-order determinant of cultural alignment: country cues often shift answers substantially, but not all shifts move toward matched human response distributions. Third-person forecasting yields the strongest directional alignment for three of the four hosted models, while personalization and role-play are weaker or less stable. Alignment gains concentrate on salient value dimensions such as religiosity, gender roles, and work-oriented material values, whereas institutional trust and democracy-related questions remain difficult. These results show that prompt framing is not a cosmetic choice in cultural value elicitation; it changes both model behavior and measured alignment.

Related papers

• Do LLMs Have Values? A Quantitative Analysis and Alignment Framework for Values in Large Language Models [49.45276299939287]
  Large Language Models (LLMs) increasingly handle complex subjective tasks.<n>LLMs fluctuate unpredictably under minor wording changes.<n> resolving this duality is critical for reliable AI alignment.
  arXiv  Detail & Related papers  (2026-09-15T03:35:04Z)
• How Identity and Opinion Shape Political Sycophancy in LLMs [17.0131514909503]
  We introduce a framework that disentangles two distinct triggers of political sycophancy: opinion and identity.<n>Using 450 manually-checked political dilemmas as controlled probes, we evaluate 13 instruction-tuned Large Language Models.<n>Our results suggest that LLM political stance is interactively and steerably vulnerable rather than being a fixed trait.
  arXiv  Detail & Related papers  (2026-08-29T11:12:03Z)
• Scenario-based Probing and Steering Cultural Values in Large Language Models--Extended Version [5.97627108250811]
  We propose a framework for probing and steering latent cultural representations in large language models.<n>By translating social value questions into scenario-based behavioral dilemmas, we extract token-level probabilities to measure implicit values.<n>We find substantial variation in steerability and identify latent entanglement, where interventions along one cultural dimension shifts along another.
  arXiv  Detail & Related papers  (2026-06-09T19:44:23Z)
• Whose Norms? Disentangling Cultural and Personal Alignment in Large Language Models [66.34115263472931]
  Large language models are increasingly used for social decision-making situations that require balancing cultural norms with personal preferences.<n>We introduce PACT, the Personal-Preference and Cultural-Norm Trade-off framework, which evaluates whether models choose to follow a cultural norm or allow personal preferences.<n>Our five-country human study on PACT shows that culture-following in humans is mainly driven by scenario country, with the lowest agreement when participants judge their own cultural contexts.
  arXiv  Detail & Related papers  (2026-06-05T22:20:30Z)
• Not What, But How: A Framework for Auditing LLM Responses across Positioning, Generalization, Anthromorphism, and Maxims [54.31707479192195]
  Large language models (LLMs) are being increasingly used to answer subjective, information-seeking questions.<n>Existing evaluations for subjective cultural queries largely focus on factual correctness, ignoring how the response is framed.<n>We introduce FRANZ, an automated FRAmework for respoNse characteriZation to conduct communicative audit of LLM responses along four dimensions.
  arXiv  Detail & Related papers  (2026-06-01T17:01:31Z)
• Do LLMs Use Cultural Knowledge Without Being Told? A Multilingual Evaluation of Implicit Pragmatic Adaptation [9.245983423391364]
  We evaluate 60 culturally grounded conversational scenarios across five languages.<n>We score responses on 12 pragmatic features covering deference to authority, individual-versus-group framing, and uncertainty management.
  arXiv  Detail & Related papers  (2026-04-20T01:58:57Z)
• No Universal Courtesy: A Cross-Linguistic, Multi-Model Study of Politeness Effects on LLMs Using the PLUM Corpus [1.263738813992273]
  The Politeness Theory by Brown and Levinson and the Impoliteness Framework by Culpeper form the basis of experiments conducted across three languages.<n>Our sample consists of 22,500 pairs of prompts and responses of various types, evaluated across five levels of politeness.<n>The findings show that model performance is highly influenced by tone, dialogue history, and language.
  arXiv  Detail & Related papers  (2026-04-17T17:33:48Z)
• Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment [75.88231994139132]
  We show that even state-of-the-art models like GPT-4.1 achieve only 57.4% accuracy in predicting modal preferences.<n>We find significant pre-existing performance biases, where models better emulate young, male, Chinese, and Christian personas.
  arXiv  Detail & Related papers  (2026-04-14T15:06:13Z)
• Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation [58.01855677487771]
  We show that multilingual large language models (MLLMs) frequently exhibit inconsistent behaviour when the prompt's language changes.<n>We propose Cross-lingual Cultural Consistent Preference optimisation (C-3PO), a consensus-driven alignment framework.<n>C-3PO achieves up to a 0.10-point absolute increase in $_S$ over unaligned models, outperforming strong prompting and representation steering baselines.
  arXiv  Detail & Related papers  (2026-04-02T14:04:06Z)
• I Am Aligned, But With Whom? MENA Values Benchmark for Evaluating Cultural Alignment and Multilingual Bias in LLMs [5.060243371992739]
  We introduce MENAValues, a novel benchmark designed to evaluate the cultural alignment and multilingual biases of large language models (LLMs)<n> Drawing from large-scale, authoritative human surveys, we curate a structured dataset that captures the sociocultural landscape of MENA with population-level response distributions from 16 countries.<n>Our analysis reveals three critical phenomena: "Cross-Lingual Value Shifts" where identical questions yield drastically different responses based on language, "Reasoning-Induced Degradation" where prompting models to explain their reasoning worsens cultural alignment, and "Logit Leakage" where models refuse sensitive questions while internal probabilities reveal strong hidden
  arXiv  Detail & Related papers  (2025-10-15T05:10:57Z)
• CAPE: Context-Aware Personality Evaluation Framework for Large Language Models [8.618075786777219]
  We propose the first Context-Aware Personality Evaluation framework for Large Language Models (LLMs)<n>Our experiments reveal that conversational history enhances response consistency via in-context learning but also induces personality shifts.<n>Our framework can be applied to Role Playing Agents (RPAs) to better align with human judgments.
  arXiv  Detail & Related papers  (2025-08-28T03:17:47Z)
• Revisiting LLM Value Probing Strategies: Are They Robust and Expressive? [81.49470136653665]
  We evaluate the robustness and expressiveness of value representations across three widely used probing strategies.<n>We show that the demographic context has little effect on the free-text generation, and the models' values only weakly correlate with their preference for value-based actions.
  arXiv  Detail & Related papers  (2025-07-17T18:56:41Z)
• The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models [67.38144169029617]
  We map the sociodemographics and stated preferences of 1,500 diverse participants from 75 countries, to their contextual preferences and fine-grained feedback in 8,011 live conversations with 21 Large Language Models (LLMs)<n>With PRISM, we contribute (i) wider geographic and demographic participation in feedback; (ii) census-representative samples for two countries (UK, US); and (iii) individualised ratings that link to detailed participant profiles, permitting personalisation and attribution of sample artefacts.<n>We use PRISM in three case studies to demonstrate the need for careful consideration of which humans provide what alignment data.
  arXiv  Detail & Related papers  (2024-04-24T17:51:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.