Personalized value alignment has become increasingly important as large language models (LLMs) are expected to accommodate diverse user preferences. However, existing methods typically align model outputs with a static value profile across prompts, overlooking that the salience of value dimensions varies substantially across contexts. Inspired by Lewin’s Field Theory, which views human behavior as jointly shaped by personal dispositions and situational constraints, we model personal values as priors and context-dependent preferences as posteriors. We propose BaCVA, an inference-time Bayesian Context-aware personalized Value Alignment method that approximates posterior personalized preferences by integrating static personal values with scenario-specific value salience. BaCVA first estimates contextual value salience from generally normative responses, and then employs a dual-view personalization module to infer posterior preferences from complementary personal-value and scenario-driven perspectives. This Bayesian formulation enables more accurate and adaptive personalized value alignment while improving data efficiency via prior values. Extensive experiments on benchmarks demonstrate its superiority over strong baselines.

As LLMs evolve from standalone assistants into autonomous agents, they increasingly operate in populations where communication, coordination, and competition form community-like interaction networks. In such settings, individually value-steered behaviors can accumulate into group-level dynamics, raising the question of how human values shape collective outcomes in interacting LLM-agent populations. We introduce CIVA, a community-level value analysis framework that varies value direction and prevalence, then measures how these interventions propagate through repeated interactions into macro-level dynamics and micro-level behaviors. We apply it in a resource-constrained multi-agent community where LLM agents autonomously communicate, cooperate, and compete. The value scan reveals distinct structural response patterns: activation-supporting values such as benevolence supports persistence and resilience, an activation-constraining value such as tradition regulates growth and cohesion, and a bidirectionally disruptive value such as power perturbs collective regimes in both directions. At the micro level, representative interventions induce emergent behaviors including deception, betrayal, self-preservation, and power-seeking. These results show that human values can act as population-level structural variables in agent communities and motivate evaluation for multi-agent value alignment.

Current cultural evaluations for large language models (LLMs) often reduce culture to single-turn factual recall via MCQs, failing to capture a common use case: users seeking practical help over multiple turns in culturally grounded scenarios. We introduce CultureConverse, a scalable, multilingual simulation and evaluation harness for culturally grounded assistant dialogue that covers 10 East and Southeast Asian regions, 58 subgroup identities, and 7 domains. Each simulated and evaluated episode produces a scored interaction where the assistant assists the user and infers cultural constraints from partial information. The resulting CultureConverse-DS dataset contains 14,610 benchmark (evaluation) episodes and 274,295 oracle-guided (gold-mode) dialogues. In our benchmark evaluation of 18 models, GPT-5 mini achieves the highest assistance quality. Human annotation experiments suggest that our evaluation framework is a sufficient proxy for human judgment. Performance gains from fine-tuning on 27,860 high-quality CultureConverse-DS samples improve in-domain assistance and transfer out-of-domain to cultural MCQ and safety classification benchmarks. We release the harness, both splits, and judge prompts to support interactive evaluation of cultural competency.

Measuring the value orientations of LLMs is helpful for diagnosing model risk and understanding their misalignment behavior. However, existing value benchmarks are often AI-generated and lack validity verification, leading to noisy and unreliable results. Inspired by Psychometrics, we propose a novel Subset Item Response Theory (S-IRT) model for assessing LLMs’ values under any value theories. S-IRT directly models the subset of values jointly expressed in each LLM response, rather than reducing responses to binary outcomes as in traditional IRT-based ability assessment. Meanwhile, it jointly estimates the LLM’s latent values and captures the relative priorities among them. Based on it, we further design a tailored information reward that estimates the information gain of each item in reducing uncertainty about the evaluated model’s value, and thus helps select a small set of high-quality items from existing benchmarks. Extensive experiments on 23 popular LLMs and three value systems (21 value dimensions) demonstrate S-IRT can be applied to multiple value systems simultaneously and improve evaluation reliability and validity. The information reward also helps reduce the number of questions to as few as 200 while preserving comparable evaluation effectiveness.

The global deployment of Large Language Models (LLMs) underscores the urgent need to evaluate their cultural alignment. However, assessing genuine “cultural awareness” across modalities (text, vision, speech) and languages remains a significant challenge. To comprehensively investigate this domain, we propose MMAC, a systematic framework that encompasses a tri-modally aligned cultural benchmark creation pipeline and a five-dimensional evaluation protocol to assess cross-country awareness disparities, evaluate cross-lingual and cross-modal consistency, and verify cultural knowledge generalization and grounding validity. Given the prevailing Western cultural bias in current models, we focus on 8 Asian countries as our dataset foundation to more acutely reveal potential cultural deficiencies in LLMs. Our dataset, MMAC-bench, features 27,000 human-curated questions across 10 languages. Crucially, it is the first dataset aligned at the input level across text, image, and speech, enabling direct cross-modal transfer tests. Each question consists of multiple-choice options accompanied by open-ended generated explanations, where 79% require multi-step reasoning grounded in cultural context, moving beyond simple memorization. We probe the causes of modal divergence, offering insights into fostering culturally robust MLLMs.

Reinforcement Learning from Human Feedback (RLHF) has emerged as a crucial technique for aligning large language models (LLMs) with human preferences. However, existing RLHF methods face key challenges, including poor sample efficiency, high computational overhead, and slow convergence. Recent studies highlight the importance of data selection in RL, but how to effectively select the most beneficial experiences for RL training remains an open problem. Existing data selection methods for RL rely on heuristic metrics, failing to establish an interpretable connection between data and optimization objectives. To address this problem, we propose InfOES (Influence-based Online Experience Selection), a novel data selection method for RLHF that dynamically estimates the influence of individual training samples on policy optimization. By incorporating data attribution into the policy gradient, InfOES can identify and filter out detrimental samples on the fly, ensuring effective convergence toward alignment objectives. Our approach is compatible with various RL algorithms (e.g., PPO, GRPO, REINFORCE++). Extensive experiments demonstrate that InfOES significantly enhances training effectiveness, achieving superior alignment performance with fewer optimization steps.

Despite their global prevalence, many Large Language Models (LLMs) are aligned to a monolithic, often Western-centric set of values. This paper investigates the more challenging task of fine-grained value alignment: examining whether LLMs can emulate the distinct cultural values of demographic subgroups. Using Singapore as a case study and the World Values Survey (WVS), we examine the value landscape and show that even state-of-the-art models like GPT-4.1 achieve only 57.4% accuracy in predicting subgroup modal preferences. We construct a dataset of over 20,000 samples to train and evaluate a range of models. We demonstrate that simple fine-tuning on structured numerical preferences yields substantial gains, improving accuracy on unseen, out-of-distribution subgroups by an average of 17.4%. These gains partially transfer to open-ended generation. However, we find significant pre-existing performance biases, where models better emulate young, male, Chinese, and Christian personas. Furthermore, while fine-tuning improves average performance, it widens the disparity between subgroups when measured by distance-aware metrics. Our work offers insights into the limits and fairness implications of subgroup-level cultural alignment.

Identifying human morals and values embedded in language is essential to empirical studies of communication. However, researchers often face substantial difficulty navigating the diversity of theoretical frameworks and data available for their analysis. Here, we contribute MoVa, a well-documented suite of resources for generalizable classification of human morals and values, consisting of (1) 16 labeled datasets and benchmarking results from four theoretically-grounded frameworks; (2) a lightweight LLM prompting strategy that outperforms fine-tuned models across multiple domains and frameworks; and (3) a new application that helps evaluate psychological surveys. In practice, we specifically recommend a classification strategy, all@once, that scores all related concepts simultaneously, resembling the well-known multi-label classifier chain. The data and methods in MoVa can facilitate many fine-grained interpretations of human and machine communication, with potential implications for the alignment of machine behavior.

The application scope of Large Language Models (LLMs) continues to expand, leading to increasing interest in personalized LLMs that align with human values. However, aligning these models with individual values raises significant safety concerns, as certain values may correlate with harmful information. In this paper, we identify specific safety risks associated with value-aligned LLMs and investigate the psychological principles behind these challenges. Our findings reveal two key insights. (1) Value-aligned LLMs are more prone to harmful behavior compared to non-fine-tuned models and exhibit slightly higher risks in traditional safety evaluations than other fine-tuned models. (2) These safety issues arise because value-aligned LLMs genuinely generate text according to the aligned values, which can amplify harmful outcomes. Using a dataset with detailed safety categories, we find significant correlations between value alignment and safety risks, supported by psychological hypotheses. This study offers insights into the “black box” of value alignment and proposes in-context alignment methods to enhance the safety of value-aligned LLMs.

As Large Language Models (LLMs) advance, aligning them with human values is critical for their responsible development. Value principles serve as the foundation for clarifying alignment goals.Multiple sets of value principles have been proposed, such as HHH (helpful, honest, harmless) and instructions for data synthesis in reinforcement learning from AI feedback (RLAIF). However, most of them are heuristically crafted, without consideration of three primary challenges in practical LLM alignment: 1) Comprehensiveness to deal with diverse and even unforeseen scenarios in which LLMs could be applied; 2) Precision to provide LLMs with clear and actionable guidance in specific scenarios; and 3) Compatability to avoid internal contracts between principles.In this paper, we formalize quantitative metrics to evaluate value principles along the three desirable properties. Building on these metrics, we propose the Hierarchical Value Principle framework (HiVaP), which constructs a hierarchical principle set and retrieves principles tailored to each scenario in a cascading way, addressing above challenges.Experimental results validate that the three metrics capture the effectiveness of value principles for LLM alignment, and our HiVaP framework that enhances these metrics leads to superior alignment. Warning: This paper contains several toxic and offensive statements.

As large language models (LLMs) are gradually integrated into human daily life, assessing their underlying values becomes essential for understanding their risks and alignment with specific preferences. Despite growing efforts, current value evaluation methods face two key challenges. C1. Evaluation Validity: Static benchmarks fail to reflect intended values or yield informative results due to data contamination or a ceiling effect. C2. Result Interpretation: They typically reduce the pluralistic and often incommensurable values to one-dimensional scores, which hinders users from gaining meaningful insights and guidance. To address these challenges, we present Value Compass Benchmarks, the first dynamic, online and interactive platform specially devised for comprehensive value diagnosis of LLMs. It (1) grounds evaluations in multiple basic value systems from social science; (2) develops a generative evolving evaluation paradigm that automatically creates real-world test items co-evolving with ever-advancing LLMs; (3) offers multi-faceted result interpretation, including (i) fine-grained scores and case studies across 27 value dimensions for 33 leading LLMs, (ii) customized comparisons, and (iii) visualized analysis of LLMs’ alignment with cultural values. We hope Value Compass Benchmarks serves as a navigator for further enhancing LLMs’ safety and alignment, benefiting their responsible and adaptive development.