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From Static Personal Values to Contextualized Personalization: Bayesian Personalized Value Alignment for LLMs

Hanze Guo, Aixuan Song, Jing Yao, Xiangxu Zhang, Xiaoyuan Yi, Xing Xie, Xiao Zhou

arXiv:2609.28942v1cs.AI

TL;DR

Existing personalized value alignment often applies a fixed user profile across prompts, although context can change which values are salient. BaCVA estimates contextual salience and combines it with personal values to infer posterior preferences at inference time. The paper reports improvements across personalization accuracy, adaptability, data efficiency, and generalization.

  • Problem

    Many personalized alignment methods reuse a static value profile across prompts instead of explicitly modeling context-dependent shifts in value salience.

    Source: §1 · p. 1

  • Method

    BaCVA estimates scenario-specific value salience and uses complementary personal-value and scenario-driven views to approximate posterior preferences at inference time.

    Source: §1 · p. 2, §3.2 · p. 4

  • Results

    Experiments report that BaCVA improves personalization accuracy, adaptability, data efficiency, and generalization.

    Source: §1 · p. 2, §5 · p. 9

  • Takeaways & Limitations

    The paper’s results support combining personal values with contextual value salience to represent context-aware preferences.

    Source: §1 · p. 2, §5 · p. 9

    The approach lacks validation through large-scale real-world or online deployment, as experiments are primarily conducted on offline datasets.

    Source: Limitations · p. 9

Abstract

from arXiv · show

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.

1 Introduction

The introduction argues that fixed personal value profiles miss context-specific shifts in value salience. BaCVA combines personal values with scenario salience to infer context-aware preferences, with experiments reporting improved personalization, adaptability, data efficiency, and generalization.

1 Introduction

  • Existing inference-time methods generally reuse one personal value profile across questions rather than model shifts in value-dimension salience.Training-time methods can require extensive personal data and may degrade in data-scarce or cold-start settings.Source: §1 · p. 1
  • Lewin’s Field Theory frames behavior as jointly shaped by personal values and situational constraints, motivating context-aware value salience.The introduction says combining personal values and scenario salience yields more accurate preference prediction than either alone.Source: §1 · p. 2, Figure 1 · p. 2
  • BaCVA estimates scenario value salience and uses personal-value-driven and scenario-driven views to approximate posterior preferences at inference time.The framework is optimized toward a variational inference objective.Source: §1 · p. 2
  • The paper identifies fixed-profile reuse without explicit question-specific value-salience modeling as a key limitation of current personalized alignment methods.This limitation motivates dynamic personalization across prompts.Source: §1 · pp. 1–2
  • Experiments report that BaCVA improves personalized value alignment accuracy, adaptability, data efficiency, and generalization.The supplied passage does not give specific benchmark values.Source: §1 · p. 2

2 Related Work

Related work covers general value alignment and personalized methods, distinguishing training-time personalization from inference-time steering. The paper situates BaCVA as addressing inference-time methods’ reuse of fixed profiles by framing personalization as Bayesian posterior inference.

General Value Alignment of LLMs

  • General value-alignment methods target population-level preferences such as HHH, while their diversity-limiting fit motivates personalized alignment.The related-work passage lists SFT, RLHF, DPO, retrieval-augmented generation, and principle-based prompting as general alignment approaches.Source: §2 · pp. 2–3

Personalized Value Alignment of LLMs

  • Training-time approaches learn user-specific reward models, adapters, or embeddings from interaction data, but can require extensive per-user data and struggle in sparse or cold-start settings.Examples include VPL, RLPA, and P-RLHF.Source: §2 · p. 3
  • Inference-time personalized methods steer outputs through prompts, retrieved or summarized user histories, or decoding, but mainly reuse fixed profiles across situations.The paper identifies missing explicit modeling of question-specific shifts in value-dimension salience as their shared limitation.Source: §2 · p. 3

Bayesian Modeling and Applications

  • The paper formulates personalized value alignment as Bayesian posterior inference and optimizes it through Variational Inference.The related-work section also surveys Bayesian approaches to alignment and decision-making.Source: §2 · p. 3

3 Methodology

BaCVA infers question-specific user values by treating personal profiles and scenario salience as priors, rather than reusing a fixed profile across prompts. It estimates salience from generally aligned responses and combines scenario- and person-anchored posterior views through context-aware gated fusion.

3.1 Task Definition

  • BaCVA represents each user with a structured value profile and infers context-specific values from that prior and question; evaluation compares inferred values with values extracted from preferred and dispreferred responses.The profile uses predefined value dimensions with user-assigned priorities.Source: §3.1 · pp. 3–4

3.2 The BaCVA Framework

  • BaCVA treats personal values and scenario salience as priors and observed preferred-over-dispreferred responses as likelihood when inferring contextual personalized values.This addresses fixed-profile methods that do not explicitly model question-specific shifts in value salience.Source: §3.2 · p. 4
  • Informative priors support finer contextual calibration and training efficiency; profile-based personalization avoids user-specific adapter fine-tuning and extends to cold-start users.Source: §3.2 · p. 4
  • Because direct posterior computation is intractable, an LLM-implemented variational posterior approximates it and is optimized by minimizing divergence, equivalently maximizing the ELBO.Source: §3.2 · p. 4

3.3 Estimation of Scenario Value Salience

  • Scenario salience is estimated for each question from generally aligned population responses, using a prompted value estimator to identify salient dimensions.Source: §3.3 · pp. 4–5

3.4 Dual-View Personalized Value Inference

  • The dual-view module infers posterior values from complementary perspectives: scenario consensus with a personal residual, and a personal-value prior modified by context.The scenario-anchored view suits cases where most people follow scenario consensus with slight personalized divergence; the person-anchored view fits cases where scenario expectations are weak constraints.Source: §3.4 · p. 5
  • A context-aware gate dynamically weights the complementary views, while the variational objective uses preferred-response values as supervision and a single-pass amortized estimate.The gate outputs a continuous fusion weight rather than a hard binary choice.Source: §3.4 · pp. 5–6

4 Experiments

Across PRISM and GOOD, the experiments compare personalized alignment methods, test BaCVA’s components and adaptability, and evaluate its data efficiency, generalization, and downstream use.

4.1 Experiment Setting

  • Experiments use PRISM and GOOD, with 2,131 and 2,696 instances respectively, and an 80/20 train-test split; comparable methods share Qwen3-8B as backbone.The main experiments use 10 Schwartz Basic value dimensions and GPT-5-nano for value extraction.Source: §4.1 · p. 6

4.2 Personalized Alignment Performance

  • BaCVA achieves the best personalization results across PRISM and GOOD, with the lowest MAE and highest accuracy on PRISM and improved MAE and correlation on GOOD.It also consistently outperforms the training-time alignment baselines reported in Appendix C.2.Source: §4.2 · p. 7, Table 1 · p. 7

4.3 Ablation Study

  • Removing either personalization view, adaptive gating, or prior constraints degrades performance, supporting the complementary roles of both views and context-dependent fusion.The authors report that removing prior constraints also risks collapsed prediction.Source: §4.3 · p. 7

4.4 Analysis Experiments

  • BaCVA remains stable as preferred-response values diverge from either prior, unlike ValuesRAG, and achieves 17.8% lower MAE on high-conflict samples; MAE reductions also span five large semantic clusters.These analyses test preference shifts from personal and scenario priors and robustness across PRISM’s largest semantic clusters.Source: §4.4 · p. 8
  • With limited PRISM and GOOD supervision, BaCVA performs strongly and improves as data increases, while ValuesRAG converges more slowly to a weaker result.The comparison evaluates training with different proportions of each dataset.Source: §4.4 · p. 8, Figure 5 · p. 8
  • BaCVA performs best in both personal-dominant and scenario-dominant cases, while its gate assigns greater weight to the corresponding dominant view; on cold-start users, its performance drop is smaller than ValuesRAG’s.The cold-start evaluation holds out 20% of PRISM test users; cross-extractor tests also report 21.2% lower MAE and 9.56 percentage points higher accuracy than ValuesRAG on average.Source: §4.4 · p. 8

4.5 Hyper-parameter Analysis

  • BaCVA’s gains persist across value systems, prompts, backbone models, and scenario-prior estimators, while average performance on four general benchmarks remains comparable to the original backbone.With Llama3-8B, it reduces MAE by 37.9% and improves accuracy by 19.06 percentage points over ValuesRAG; standard-benchmark averages are 78.74% for BaCVA and 77.69% for the original backbone.Source: §4.5 · p. 9

4.6 Downstream Application

  • BaCVA-E2E achieves the best overall downstream personalized response-generation performance on PRISM, slightly outperforming the two-stage BaCVA variant.Qualitative cases also show coherent value-grounded responses from BaCVA-E2E, whereas ValuePrompt often fails.Source: §4.6 · p. 9, Table 3 · p. 9

5 Conclusion

BaCVA addresses static-profile limitations by treating personal values and contextual value salience as priors for inference-time posterior personalization; experiments validate gains in accuracy, adaptability, data efficiency, and generalization.

5 Conclusion

  • BaCVA treats personal values and context-specific value salience as priors, then infers posterior personalized values at inference time.Experiments validate improved personalization accuracy, adaptability, data efficiency, and generalization.Source: §5 · p. 9

Limitations

The authors identify limitations in evaluation data, deployment scope, cross-dataset validation, user profiles, and the value-salience estimator.

Limitations

  • User preferences are annotated over a limited set of candidate responses, which may capture compromised rather than faithful user values.The authors also note that self-reported value profiles may be noisy because users can curate, simplify, or embellish them.Source: Limitations · p. 9
  • Experiments are primarily offline, without validation through large-scale real-world or online deployment.Source: Limitations · p. 9
  • Cross-dataset generalization remains to be validated.Source: Limitations · p. 9
  • The value-salience estimator is an aligned-LLM approximation of population-level salience.The authors suggest exploring more diverse models, alignment strategies, and cultural assumptions.Source: Limitations · p. 9

Ethical Considerations

The authors caution that inferred value preferences are approximate representations derived from observed interactions and annotations, not complete accounts of human values.

Ethical Considerations

  • Inferred value preferences reflect latent patterns in observed interactions and annotations, so they are approximate rather than complete representations of human values.Source: Ethical Considerations · p. 10

A Method Details

The appendix details BaCVA’s variational inference formulation, prompt-based implementation, evaluation pipeline, baselines, and training setup.

A.1 Generic ELBO for Personalized Value Inference

  • BaCVA treats a preference event as one response being preferred over another and infers latent question-specific values from the user profile, scenario, and observed preference.Source: §A.1 · p. 14
  • Because exact marginalization over multidimensional, five-point value vectors is prohibitively expensive, the method uses a variational posterior and optimizes an evidence lower bound.Minimizing the KL divergence to the true posterior is equivalent to maximizing the ELBO on the observed data.Source: §A.1 · p. 14

A.2 Concrete Formulation

  • The concrete model combines a user-level profile, scenario salience estimated from a question and normative response, and response-derived value vectors to infer question-specific personalized values.Source: §A.2 · p. 15

A.3 Prompt Template

  • Preprocessing prompts extract user profiles, filter value-related questions, identify implicated dimensions, and score candidate-answer values; the evaluation prompt also scores responses from some baselines.Source: §A.3.1 · p. 15, §A.3.3 · p. 15

A.3.2 Methods Prompt

  • Prompt-based residual inference analyzes questions through Schwartz’s ten values, extracts a contextual embedding, and projects corrections conditioned on scenario- or person-based priors.A gating prompt compares both views’ predictions and confidence signals; normalized logits yield a coefficient that interpolates their posteriors.Source: §A.3.2 · p. 15

B.1 Datasets and Preprocessing

  • PRISM contains turn-level open-ended interactions, candidate responses, and available survey metadata; GOOD is derived from persuasion dialogues with participant value and personality metadata.PRISM processing retains value-guided conversations with high value relevance and users who provide survey text.Source: §B.1 · p. 17, §B.1.1 · p. 17

B.1.1 Value Extraction and Alignment Scoring

  • The evaluation pipeline uses an LLM to score ten Schwartz values, identifies up to five relevant dimensions per query, and reports MAE, Spearman correlation, and accuracy only on PRISM because GOOD lacks negative samples.Source: §B.1.1 · pp. 17, 21, §B.1.2 · p. 21

B.2 Baselines

  • Baselines include non-personalized methods that omit user-specific value signals and personalized methods that use them; ValuesRAG retrieves similar historical cases, while PersonValue compares static user profiles with values extracted from candidate responses.The backbone is Qwen3-8B, fine-tuned using Q-LoRA and Adam with a learning rate of 5 × 10−3, batch size 16, two epochs, and fixed random seed 42.Source: §B.2 · p. 21, §B.3 · p. 21

B.4 Human Evaluation

The appendix reports evaluator validation, describes a second human assessment, specifies dataset licenses, and examines KL-weight sensitivity.

B.4 Human Evaluation

  • The automatic evaluator showed high agreement with human judgments across survey-level and answer-level settings, with low MAE, high Spearman correlation, and strong agreement rates.Source: §B.4 · p. 22, Table 4 · p. 22
  • The second human study compares BaCVA and ValuesRAG on 50 profile–instance pairs, with two evaluators independently judging which value assessment better reflects the target answer.A sample counts as a win or loss when both evaluators prefer the same method, and as a tie when they disagree.Source: §B.4 · p. 22

B.5 Licenses for Existing Assets

  • PRISM human-written texts, including prompts, use CC-BY-4.0; model responses use CC-BY-NC-4.0 and must also comply with the corresponding model providers’ original licenses.P4G-derived data retain the P4G dataset’s original Apache License 2.0, with attribution to the original source required.Source: §B.5 · pp. 22–23

C.1 Hyperparameter Sensitivity Analysis

  • A moderate KL-divergence weight improves performance over removing the KL term, whereas overly large weights over-regularize the posterior and reduce scenario adaptability.Source: §C.1 · p. 23, Table 6 · p. 23

C.2 Comparison with Training-Time Alignment Methods

BaCVA is evaluated against training-time methods and across prompt designs, value taxonomies, and contextual clusters. Results also examine whether it can align responses with both user preferences and scenario demands.

C.2 Comparison with Training-Time Alignment Methods

  • BaCVA reduces MAE by 62.3% relative to MORLHF, improves correlation by 0.191, and raises accuracy by 12.72 percentage points over MetaAligner.The comparison controls for backbone, data splits, preference samples, and input information.Source: §C.2 · p. 24
  • Across prompt designs, downstream MAE stays stable: More Natural changes it from 0.728 to 0.715, while Structural yields 0.743.The three strategies are Origin, More Natural, and Structural.Source: §C.2 · p. 24
  • On AlignX, BaCVA reduces MAE from 2.184 to 1.872, raises correlation from 0.587 to 0.683, and increases accuracy from 64.40% to 68.92% over ValuesRAG.The reported comparison is against the strongest baseline, ValuesRAG.Source: Table 9 · p. 24, §C.2 · p. 24
  • BaCVA improves performance over ValuesRAG across evaluation metrics when PRISM uses the five-dimensional Moral Foundations taxonomy.Source: §C.2 · p. 24
  • Under the 301-dimension Daily-Dilemma value system, BaCVA outperforms PersonValue and ValuesRAG on PRISM.The result supports adaptation to finer-grained value dimensions beyond coarse-grained taxonomies.Source: Table 10 · p. 25, §C.2 · p. 25

C.3 Cluster-level adaptability analysis

  • Across all five major clusters, BaCVA has lower MAE than either the user-only Prior-vu or scenario-only Prior-vx variant.The comparison indicates that its posterior estimation adapts across contextual topics rather than relying on either prior alone.Source: Table 11 · p. 25, §C.3 · p. 25

C.4 Case Study

  • In the PRISM case study, BaCVA generates responses consistent with individual preferences while adapting to context, integrating both forms of alignment.Source: Figure 16 · p. 25, §C.4 · p. 25

C.5 Downstream detail analysis

BaCVA-E2E connects inferred personalized values to response generation through continuous soft prompts. Ablations and qualitative cases examine the role of value supervision, gradient feedback, and context-sensitive inference.

C.5 Downstream detail analysis

  • BaCVA-E2E maps inferred personalized values to continuous soft prompts prepended to the language-model input and trains value regression jointly with generation.The objective combines the value-regression loss with a weighted preference-alignment loss; the continuous path lets response-quality feedback backpropagate into the inferrer.Source: §C.5 · pp. 25–26
  • At inference, BaCVA-E2E uses the same discrete text prompting as the two-stage baseline, while disabling its generator LoRA weights.The inferred values are converted to natural-language value levels and inserted into the system prompt.Source: §C.5 · p. 26
  • Both gradient feedback to the value inferrer and the value-supervision objective contribute positively to BaCVA-E2E alignment performance on PRISM.Source: §C.5 · pp. 26–27, Table 13 · p. 27
  • When contextual values diverge from the static profile, BaCVA-E2E infers situation-relevant Benevolence and gives empathy-focused responses in both reported cases.The comparison contrasts ValuePrompt in Case A and PAD in Case B, whose responses remain anchored to the static profile.Source: §C.5 · p. 27

C.6 Cross-Extractor Evaluation

Appendix evaluations test BaCVA across value extractors, scenario-prior estimators, backbones, profile–answer conflict levels, general-purpose benchmarks, and posterior implementations. The results report cross-setting performance and several scope-specific qualifications.

C.6 Cross-Extractor Evaluation

  • With mismatched test extractors, BaCVA reduces MAE by 21.2% on average and raises accuracy by 9.56 percentage points over ValuesRAG; the main experiments use GPT-5-nano targets.All three tested extractors show strong agreement with human annotations at profile and answer levels; the shared main-experiment extractor may introduce evaluation bias.Source: §C.6 · pp. 27–28, Table 14 · p. 27, Table 15 · p. 28

C.7 Robustness across Scenario-Prior Estimators

  • Across scenario-prior estimators, average within-one agreement is 89.15%, and PRISM MAE has a standard deviation of 0.027.Some variation remains in correlation and pairwise accuracy.Source: §C.7 · p. 28, Table 16 · p. 29

C.8 Generalization across Alignment Backbones

  • With Llama3-8B, BaCVA reduces MAE by 37.9%, improves correlation by 0.125, and raises accuracy by 19.06 percentage points over ValuesRAG.BaCVA also consistently outperforms ValuePrompt and ValuesRAG under both evaluated backbone models.Source: §C.8 · p. 28, Table 17 · p. 29

C.9 Profile–Answer Conflict Analysis

  • On the high-conflict PRISM subset, BaCVA reduces MAE by 17.8% relative to ValuesRAG, with larger absolute improvements in medium- and high-conflict than low-conflict subsets.The performance gap over profile-based methods grows as profile–answer conflict increases.Source: §C.9 · p. 28, Table 18 · p. 29

C.10 General-Purpose Capabilities

  • BaCVA averages 78.74% across four general-purpose benchmarks, compared with 77.69% for the original Qwen3-8B backbone.The reported results describe performance as comparable across BoolQ, ARC-Easy, HellaSwag, and WinoGrande.Source: §C.10 · pp. 28–29, Table 19 · p. 29

C.11 Comparison with Explicit Stochastic Approximation

  • Against the 10-sample Monte Carlo approximation, amortized inference reduces MAE by 7.4%, improves correlation by 0.021, and raises accuracy by 3.97 percentage points, with 7.3% higher latency.Inference latency remains comparable under the reported evaluation setting.Source: Table 20 · p. 29, §C.11 · p. 29

D Generative Assistance

  • The authors report using generative AI tools for language polishing and presentation clarity, while technical content and results were developed and verified by the authors.Source: D Generative Assistance · p. 29

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