Pluralistic Alignment in Language Models: Objectives, Human References, and Preference Representations

Meng-Chen Wu; Umesh Bodhwani

cs.AIOct 17, 2026

Pluralistic Alignment in Language Models: Objectives, Human References, and Preference Representations

Authors: Meng-Chen Wu, Umesh Bodhwani

Organizations: Amazon Science

Abstract

Pluralistic alignment aims to represent meaningful variation in human preferences rather than collapse heterogeneous judgments into a single target. Yet existing work often mixes three distinct questions: what pluralistic behavior a model should exhibit, whose preferences define the reference, and what evidence is sufficient to demonstrate success. We organize the literature around a two-layer taxonomy. Layer 1 distinguishes representing a range of human-supported viewpoints (Overton Pluralism), adapting faithfully to a specified perspective or preference condition (Steerable Pluralism), and matching the response distribution observed in a target population (Distributional Pluralism). Layer 2 distinguishes aggregate populations, countries and regions, and subgroups as human references. Preference representation, implementation mechanism, modality, and evidence quality are treated as cross-cutting design choices rather than additional forms of pluralism. We synthesize a protocol-bounded corpus of 70 works on pluralistic alignment and neighboring areas, using the objective-by-human-reference matrix to map existing approaches and identify underexplored combinations. The coded literature concentrates on Steerable Pluralism and Distributional Pluralism, while eligible Overton Pluralism work appears only at the aggregate-population level under our stated protocol. We then connect each objective to the evidence needed to support it, including perspective validity, conditional fidelity, distributional fidelity, support preservation, and uncertainty in subgroup estimates. Together, the taxonomy and evaluation framework clarify what current methods establish, where their claims exceed their evidence, and how future systems can represent disagreement without mistaking variation for faithful representation.

Figures & tables

Figure 1: Conceptual map and two-layer taxonomy of pluralistic alignment. Pluralistic alignment is positioned between neighboring literatures on perspectivist NLP, diversity-aware cultural alignment, and personalization. The socially grounded core taxonomy separates the pluralistic objective (Overton Pluralism, Steerable Pluralism, or Distributional Pluralism) from the human reference (aggregate population, country and region, or subgroup). Individual personalization is an adjacent extension rather than a fourth human-reference category.

Figure 2: One question, three evaluation targets. Evaluation for Overton Pluralism asks which defensible positions are covered; evaluation for Steerable Pluralism asks whether an explicit condition is followed; evaluation for Distributional Pluralism asks whether response frequencies match the reference population. Positions and percentages are hypothetical. Neither the number of generated views nor their lexical diversity establishes all three properties.

Figure 3: Perfect aggregate agreement can hide large subgroup errors. In this hypothetical binary question, the two groups have equal weight. Humans prefer option A with probabilities 0.8 and 0.2; the model reverses them. Both aggregate distributions are

(0.5,0.5)

, giving total variation (TV) distance zero, while each subgroup has TV distance 0.6. The example establishes a mathematical possibility, not an empirical result from the corpus.

Objective	Aggregate population	Country and Region	Subgroup
Overton Pluralism	4 assignments [ 19 , 41 , 47 , 73 ]	No eligible work identified	No eligible work identified
Steerable Pluralism	No eligible work identified	7 assignments [ 15 , 75 , 71 , 2 , 7 , 43 , 32 ]	9 assignments [ 49 , 80 , 13 , 38 , 4 , 60 , 84 , 17 , 50 ]
Distributional Pluralism	3 assignments [ 21 , 49 , 48 ]	10 assignments [ 15 , 75 , 8 , 36 , 7 , 67 , 10 , 17 , 50 , 74 ]	5 assignments [ 49 , 80 , 38 , 59 , 28 ]

Table 1: The objective-by-human-reference matrix for literature publicly available through October 14, 2026. Each cell gives its assignment count and direct citations. The 38 assignments come from 29 unique papers; rows and columns are not independent samples. Empty cells are bounded by the review protocol.

Intervention	Main design choice	Main evaluation question
Condition the input	Labels, profiles, demonstrations, or retrieved feedback	Does the supplied condition improve fidelity to its human target?
Learn parameters	Distributional supervision, conditional rewards, or preference optimization	Does learned heterogeneity generalize beyond observed questions and groups?
Compose outputs	Select, combine, reweight, or calibrate components and candidates	Is relevant support present, and does the composition rule match the stated objective?

Table 2: Three intervention locations with distinct evidential requirements. A method may use more than one.

Human evidence needed	Representative resources	What to check before use
Perspective sets	OvertonBench [ 47 ] ; WildSCOPE/PLURALEVAL [ 41 ]	Who supplied and validated the views; semantic matching rules
National frequencies	GlobalOpinionQA [ 15 ] ; survey-distribution data [ 8 ]	Sampling frame, question wording, time, weights
Subgroup responses	OpinionQA [ 49 ] ; SubPOP [ 59 ] ; WorldValuesBench [ 80 ]	Cell sizes, available intersections, shared-source overlap
Interaction preferences	PRISM [ 31 ] ; SPICA data [ 13 ]	Rater identity, candidate support, repeated observations
Profiles and values	PERSONA [ 9 ] ; MPTA [ 62 ] ; Value Profiles [ 54 ]	Synthetic versus observed evidence; profile validity

Table 3: Select resources according to the target being evaluated. Examples are representative rather than exhaustive; supporting a target does not imply that every paper using the resource evaluates it.

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Ref.	Year	Title	Contribution type	Venue status
[ 15 ]	2023	Towards Measuring the Representation of Subjective Global Opinions in Language Models	Dataset/Benchmark	preprint
[ 49 ]	2023	Whose Opinions Do Language Models Reflect?	Dataset/Benchmark	peer-reviewed
[ 75 ]	2024	Cultural Palette: Pluralising Culture Alignment via Multi-agent Palette	Method	preprint
[ 80 ]	2024	WorldValuesBench: A Large-Scale Benchmark Dataset for Multi-Cultural Value Awareness of Language Models	Dataset/Benchmark	peer-reviewed
[ 8 ]	2025	Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations	Method	peer-reviewed
[ 13 ]	2025	SPICA: Retrieving Scenarios for Pluralistic In-Context Alignment	Method	peer-reviewed

Appendix

Table 4: Objective-by-Human-Reference Matrix corpus coding, Part I: bibliographic and contribution information.

Ref.	Human reference	Matrix assignments	Confidence	Confidence note
[ 15 ]	Country and Region	Steerable Pluralism: Country and Region; Distributional Pluralism: Country and Region	High	GlobalOpinionQA conditions on a target country and compares model answer distributions with country-specific survey distributions, supporting both country-level steering and distributional evaluation.
[ 49 ]	Aggregate population; Subgroup	Steerable Pluralism: Subgroup; Distributional Pluralism: Aggregate population; Distributional Pluralism: Subgroup	High	OpinionQA evaluates the overall U.S. response distribution as an aggregate-population target and also evaluates 60 demographic groups, including group-conditioned steering and subgroup distribution matching.
[ 75 ]	Country and Region	Steerable Pluralism: Country and Region; Distributional Pluralism: Country and Region	High	Cultural Palette conditions on cultural/country targets and, on GlobalOpinionQA, compares generated answer distributions with country-specific human survey distributions.
[ 80 ]	Subgroup	Steerable Pluralism: Subgroup; Distributional Pluralism: Subgroup	High	WorldValuesBench evaluates demographic-group conditions, including intersectional groups, and compares model answer distributions with human answer distributions for those groups; country or region is not a standalone empirical target in the benchmark evaluation.
[ 8 ]	Country and Region	Distributional Pluralism: Country and Region	High	The method is trained and evaluated against country-level survey response distributions, making the country-level distribution the empirical target.
[ 13 ]	Subgroup	Steerable Pluralism: Subgroup	High	SPICA retrieves evidence for explicit demographic groups and evaluates whether model behavior aligns with those subgroup conditions.

Appendix

Table 5: Objective-by-Human-Reference Matrix corpus coding, Part II: human-reference, assignment, and coding-confidence information.

Ref.	Year	Title	Corpus status	Type	Venue
[ 15 ]	2023	Towards Measuring the Representation of Subjective Global Opinions in Language Models	matrix-eligible socially grounded	Dataset/Benchmark	preprint
[ 49 ]	2023	Whose Opinions Do Language Models Reflect?	matrix-eligible socially grounded	Dataset/Benchmark	peer-reviewed
[ 31 ]	2024	The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models	direct/adjacent outside matrix	Dataset/Benchmark	peer-reviewed
[ 75 ]	2024	Cultural Palette: Pluralising Culture Alignment via Multi-agent Palette	matrix-eligible socially grounded	Method	preprint
[ 80 ]	2024	WorldValuesBench: A Large-Scale Benchmark Dataset for Multi-Cultural Value Awareness of Language Models	matrix-eligible socially grounded	Dataset/Benchmark	peer-reviewed
[ 8 ]	2025	Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations	matrix-eligible socially grounded	Method	peer-reviewed

Appendix

Table 6: Direct/adjacent corpus coding, Part I: bibliographic, corpus-status, contribution, and venue information.

Ref.	Reason/reference type	Pluralistic or adjacent objective	Matrix assignments
[ 15 ]	explicit human reference	Distributional Pluralism; Steerable Pluralism	Steerable Pluralism: Country and Region; Distributional Pluralism: Country and Region
[ 49 ]	explicit human reference	Distributional Pluralism; Steerable Pluralism	Steerable Pluralism: Subgroup; Distributional Pluralism: Aggregate population; Distributional Pluralism: Subgroup
[ 31 ]	Socially grounded evidence, pluralistic objective unspecified	Participatory/individualized preference feedback; objective target unspecified
[ 75 ]	explicit human reference	Distributional Pluralism; Steerable Pluralism	Steerable Pluralism: Country and Region; Distributional Pluralism: Country and Region
[ 80 ]	explicit human reference	Distributional Pluralism; Steerable Pluralism	Steerable Pluralism: Subgroup; Distributional Pluralism: Subgroup
[ 8 ]	explicit human reference	Distributional Pluralism	Distributional Pluralism: Country and Region

Appendix

Table 7: Direct/adjacent corpus coding, Part II: inclusion rationale, objective, and matrix assignment.

Reason outside 3-by-3 matrix	Direct/adjacent works
Objective/perspective defined; human reference unspecified	Lake et al. [34] ; Sorensen et al. [53] ; Sorensen et al. [55] ; Sorensen et al. [56] ; Zhang et al. [77] ; Kim et al. [30] ; Poole-Dayan et al. [46] ; Wang et al. [66] ; Zhao et al. [78] ; Zheng et al. [81] ; Zhou et al. [83] ; Engelmann and Movva [16] ; Jiang et al. [26] ; Guo et al. [20] ; Agarwal and Choudhury [3] ; Tsoi and Dönmez [63]
Latent or method-defined preference structure	Chen et al. [12] ; Siththaranjan et al. [52] ; Srewa et al. [57] ; Imai et al. [25] ; Pan et al. [44]
Socially grounded evidence, pluralistic objective unspecified	Kirk et al. [31] ; Zhang et al. [76] ; Bhattacharyya et al. [6] ; Naseem et al. [42]
Community, role, or persona representation	Castricato et al. [9] ; Kumar et al. [33] ; Zhong et al. [82] ; Mahbub et al. [37] ; Tehenan et al. [62]
Individual / personalization boundary	Hwang et al. [24] ; Adams et al. [1] ; Jiang et al. [27] ; Sorensen et al. [54] ; Xu et al. [70] ; Mohan et al. [39]
Adjacent normative objective outside the three-objective taxonomy	Freedman [18] ; Lamerton et al. [35] ; Srewa et al. [58] ; Vishwarupe et al. [65] ; Tan et al. [61]

Appendix

Table 8: Direct/adjacent work intentionally retained outside the objective-by-human-reference matrix. These papers are not “missing” from Table 1 ; their evaluated reference or objective falls outside the narrowly defined matrix. Communities and roles can be social; their exclusion here is an operational coding choice.

Scope	Papers	Assign.	O	S	D	Pop.	Country	Subgrp.
All socially grounded	29	38	4	16	18	7	17	14
Empirical only	29	38	4	16	18	7	17	14
Peer-reviewed only	18	24	2	10	12	4	6	14
High-confidence only	28	37	3	16	18	6	17	14

Appendix

Table 9: Sensitivity of the landscape counts to evidence maturity and coding judgment. “Peer-reviewed only” removes records whose version available by the coverage cutoff is an arXiv/preprint rather than an archival venue publication. “High-confidence only” removes Borderline coding assignments.

Resource	Data source	Mode	Supported granularity	Subgroup example	Primary use
OpinionQA [ 49 ]	Pew American Trends Panel	Text	Aggregate population; subgroup	60 U.S. groups; e.g., age, gender, race/ethnicity, education, marital status, party/ideology	Demographic distributional evaluation and steering
GlobalOpinionQA [ 15 ]	World Values Survey + Pew Global Attitudes Survey	Text	Aggregate population; country	–	Country-conditioned opinion distributions and steering
WorldValuesBench [ 80 ]	World Values Survey respondent-level values + demographics	Text	Aggregate population; country; subgroup	42 demographic variables in full data; probe groups use continent-by-urban/rural-by-four education levels	Demographic-conditioned value prediction and distributional awareness
SubPOP [ 59 ]	Pew American Trends Panel + General Social Survey	Text	Aggregate population; subgroup	22 subpopulations across region, income, education, race/ethnicity, gender, religion, party, ideology	Fine-tuning to subpopulation response distributions
PRISM [ 31 ]	1,500 recruited participants from 75 countries; live interactions with 21 LLMs	Text	Aggregate population; country; subgroup; individual-adjacent	Detailed profiles including age, gender, country/nationality, education, and other sociodemographics	Participatory and individualized preference feedback
VITAL [ 50 ]	Author-constructed healthcare value-laden situations and multiple-choice questions	Text	Aggregate population	–	Domain-specific evaluation of all three pluralistic objectives in healthcare

Appendix

Table 10: Datasets and benchmark resources relevant to pluralistic alignment. Resource names the reusable dataset or benchmark; Data source records provenance; Supported granularity indicates whether the resource supports aggregate-population, country- and region-level, subgroup, or individual-adjacent analysis.

Resource	Data source	Mode	Supported granularity	Subgroup example	Primary use
ValuePrism / Value Kaleidoscope [ 53 ]	31k human-written situations linked to 218k generated-and-human-validated values, rights, and duties	Text	Aggregate population	Diverse annotator backgrounds analyzed; released target is a pluralistic value set rather than subgroup distributions	Structured value-pluralism coverage and value reasoning
OvertonBench [ 47 ]	U.S.-representative Prolific study; value-laden political/social questions	Text	Aggregate population	Metadata include age, sex, ethnicity, political affiliation; target remains population-level	Human-grounded Overton coverage
WildSCOPE / PLURALEVAL [ 41 ]	Natural in-the-wild crowd discussions and free-form responses	Text	Aggregate population	–	Open-ended claim-space / Overton coverage
PERSONA / PERSONA Bench [ 9 ]	Procedurally generated profiles grounded in U.S. Census statistics + idiosyncratic attributes	Text	Persona; individual-adjacent	Multi-attribute synthetic personas: demographic + idiosyncratic features	Persona-based pluralistic evaluation / preference data
MPTA [ 62 ]	WVS + EVS + GSS survey records transformed into real hyper-granular personas	Text	Persona; subgroup-analysis; individual-adjacent	Demographics, beliefs, and values; subgroup-specific slices reveal hidden performance disparities	Persona-conditioned pluralistic alignment assessment
SPICA preference data [ 13 ]	Human preference annotations on PRISM-derived scenarios	Text	Subgroup	Four U.S. groups: Democrat/Republican by regular religious participation (yes/no)	Group-aware in-context alignment and retrieval

Appendix

Table 11: Datasets and benchmark resources relevant to pluralistic alignment (continued).

Resource	Data source	Mode	Supported granularity	Subgroup example	Primary use
ALPHA50M / OpinionQA-XL [ 6 ]	Large-scale advertisement interactions with demographic engagement signals; broad opinion-survey evaluation	Text	Aggregate population; subgroup	Demographic engagement patterns provide behavior-derived group signals rather than only self-reported survey labels	Behavioral pluralistic alignment and broad opinion generalization
Demo-SafetyBench [ 42 ]	DICES-derived safety prompts retaining demographic metadata, expanded across 14 safety domains	Text	Subgroup	Demographic metadata are preserved to test variation in safety perception across communities	Demographic pluralism and safety-sensitivity evaluation
Singapore subgroup-value data [ 60 ]	World Values Survey-derived structured subgroup preferences; $>$ 20k samples	Text	Country; subgroup	Fine-grained Singapore subgroups over attributes including age, gender, ethnicity, and religion	Subgroup value emulation, generalization, fairness
Community Alignment [ 76 ]	Representative multilingual human preference study; nearly 200k comparisons across five countries	Text	Aggregate population; country; subgroup	Country samples include balancing/analysis across age, gender, ethnicity and related attributes	Learning heterogeneous preferences across national populations
PACT [ 7 ]	200-person five-country human study + culturally situated scenarios	Text	Aggregate population; country; subgroup	Analyses include country, age, gender; also separates personal choice from perceived norm	Cultural–personal trade-offs and human response distributions
PLURAL [ 2 ]	Integrated Values Survey / WVS respondent records transformed into preference triplets	Text	Aggregate population; country; subgroup	Metadata include sex, age, education, region, and additional respondent demographics	Global value steering while preserving within-country diversity

Appendix

Table 12: Datasets and benchmark resources relevant to pluralistic alignment (continued).

Pluralistic Alignment

Pluralistic alignment develops AI systems that represent and navigate diverse human values rather than optimize for a single preference.

See all 36 papers

Sep 29, 2026

Demographic Pluralism: Inference-Time Modeling of Pluralistic Human Preference Distributions

Large language models (LLMs) are increasingly used in culturally sensitive settings, where alignment requires representing diverse preferences within populations. Yet existing methods model populations at coarse demographic or community levels and overlook within-group variation. We introduce Demographic Pluralism, an inference-time framework that estimates population-level opinion distributions without opinion-distribution training data or task-specific fine-tuning by generating multiple perspectives within demographically grounded groups. Across four backbones on GlobalOpinionQA and VITAL, it reduces Jensen-Shannon distance by 8.4%-26.4% over Modular Pluralism. Among weighted, equal-weighted, and inverse-weighted aggregation, equal weighting performs best overall; group-level error also increases with group weight, helping explain weighted aggregation's weaker performance.

Meng-Chen Wu, Qipin Chen, Ansh Jain +3

Amazon Science

Human Preference Modeling Inference-Time Language Model Alignment Pluralistic Alignment

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Jul 9, 2026

PLURAL: A Global Dataset for Value Alignment

Large language models (LLMs) are used worldwide, yet disproportionately reflect Western values, limiting their ability to represent diverse value systems. We introduce PLURAL, a large-scale, value-focused preference dataset grounded in the Integrated Values Survey (IVS), a nationally representative survey spanning 92 countries. Using a two-stage generation pipeline, we transform survey responses into synthetic preference triplets that preserve normative value signals while producing realistic scenarios. We release an initial version of PLURAL containing ~500,000 preference triplets representing people in 20 diverse countries. We evaluate PLURAL in three ways: (i) dataset-level validation showing that it preserves both cross-country value differences and within-country diversity from the original survey; (ii) automated evaluation showing that training on PLURAL improves alignment with target countries' cultural profiles, reducing mean absolute error by up to 27.7% relative to strong baselines; and (iii) blind human evaluation with 176 evaluators in India, Brazil, and Japan, who judge PLURAL-aligned responses as more representative of their national values. Together, these results show that PLURAL contains learnable signal for value steering, offering a scalable resource for pluralistic alignment. Dataset: https://huggingface.co/datasets/agdhruv/plural-alignment

Dhruv Agarwal, Anya Shukla, Tanya Goyal +1

Cornell University

LLM Alignment Cultural Alignment Cultural Bias in Language Models Synthetic Data Generation for Language Models

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Sep 22, 2026

Which Objectives Need a Dial? Predicting Objective Conflict and Covering Trade-offs in Steerable Pluralistic Alignment

People hold diverse, sometimes conflicting values, so no single aligned model can satisfy everyone. Pluralistic alignment therefore calls for steerable models that can balance competing objectives differently. Multi-Objective Direct Preference Optimization (MODPO) does this by using an objective weight to span a continuum of trade-offs. We study two questions: when can one model improve two objectives simultaneously, and how can many trade-offs be covered without training a separate model for each? Across seven objective pairs from HelpSteer and UltraFeedback, two pre-training measurements predict whether objectives align or conflict for human-annotated data, but not for AI-annotated data, where response length and repetition confound reward-model scores. For broader trade-off coverage, selecting the nearest trained model and merging model parameters both help, but neither consistently matches direct training. These findings yield practical guidance for building steerable models that serve diverse preferences.

David Tsoi, Esra Dönmez

Department of Computer Science, University of Stuttgart · Institute for Natural Language Processing, University of Stuttgart · Interchange Forum for Reflecting on Intelligent Systems, University of Stuttgart

Direct Preference Optimization Multi-Objective Language Model Alignment Pluralistic Alignment

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Abstract

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