Polypersona: Persona-Grounded LLM for Synthetic Survey Responses

• URL: http://arxiv.org/abs/2512.14562v1
• Date: Tue, 16 Dec 2025 16:33:23 GMT
• Title: Polypersona: Persona-Grounded LLM for Synthetic Survey Responses
• Abstract summary: PolyPersona is a generative framework for synthesizing persona-conditioned survey responses across multiple domains.<n>We construct a dataset of 3,568 synthetic survey responses spanning ten domains and 433 distinct personas.<n>Results show that compact models such as TinyLlama 1.1B and Phi-2 achieve performance comparable to larger 7B to 8B baselines.
• Score: 1.3225135098402387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces PolyPersona, a generative framework for synthesizing persona-conditioned survey responses across multiple domains. The framework instruction-tunes compact chat models using parameter-efficient LoRA adapters with 4-bit quantization under a resource-adaptive training setup. A dialogue-based data pipeline explicitly preserves persona cues, ensuring consistent behavioral alignment across generated responses. Using this pipeline, we construct a dataset of 3,568 synthetic survey responses spanning ten domains and 433 distinct personas, enabling controlled instruction tuning and systematic multi-domain evaluation. We evaluate the generated responses using a multi-metric evaluation suite that combines standard text generation metrics, including BLEU, ROUGE, and BERTScore, with survey-specific metrics designed to assess structural coherence, stylistic consistency, and sentiment alignment.Experimental results show that compact models such as TinyLlama 1.1B and Phi-2 achieve performance comparable to larger 7B to 8B baselines, with a highest BLEU score of 0.090 and ROUGE-1 of 0.429. These findings demonstrate that persona-conditioned fine-tuning enables small language models to generate reliable and coherent synthetic survey data. The proposed framework provides an efficient and reproducible approach for survey data generation, supporting scalable evaluation while facilitating bias analysis through transparent and open protocols.

Related papers

• PersonaForge: Realistic Multi-Turn User Simulation for Agentic Systems [53.65117408512214]
  We introduce textbfPersonaForge, a user simulation framework for synthesizing realistic multi-turn user--agent interactions.<n>Using PersonaForge, we construct a 6.3K-record training dataset and textbfPersonaForge-Bench, a manually annotated 138-task benchmark.<n>Experiments show that PersonaForge training improves the composite score by +4.1%, with gains across all four dimensions.
  arXiv  Detail & Related papers  (2026-08-28T14:33:19Z)
• HuggingR$^{4}$: A Progressive Reasoning Framework for Discovering Optimal Model Companions [50.61510609116118]
  HuggingR$4$ is a novel framework that combines Reasoning, Retrieval, Refinement, and Reflection to efficiently select models.<n>It attains a workability rate of 92.03% and a reasonability rate of 82.46%, surpassing existing method by 26.51% and 33.25% respectively.
  arXiv  Detail & Related papers  (2025-11-24T03:13:45Z)
• Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble [46.82793004650415]
  Large language models (LLMs) have demonstrated promise in emulating human-like responses across a range of tasks.<n>We propose a novel alignment framework that treats LLMs as agent proxies for human survey respondents.<n>We introduce P2P, a system that steers LLM agents toward representative behavioral patterns using structured prompt engineering, entropy-based sampling, and regression-based selection.
  arXiv  Detail & Related papers  (2025-09-14T15:08:45Z)
• Emulating Public Opinion: A Proof-of-Concept of AI-Generated Synthetic Survey Responses for the Chilean Case [0.0]
  Large Language Models (LLMs) offer promising avenues for methodological and applied innovations in survey research.<n>We evaluate the reliability of LLM-generated synthetic survey responses against ground-truth human responses from a Chilean public opinion probabilistic survey.
  arXiv  Detail & Related papers  (2025-09-11T21:43:59Z)
• Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation [18.225151370273093]
  This paper explores a new paradigm: simulating virtual survey respondents using Large Language Models (LLMs)<n>We introduce two novel simulation settings, namely Partial Attribute Simulation (PAS) and Full Attribute Simulation (FAS)<n>We curate a comprehensive benchmark suite, LLM-S3 (Large Language Model-based Sociodemographic Simulation Survey), that spans 11 real-world public datasets across four sociological domains.
  arXiv  Detail & Related papers  (2025-09-08T04:59:00Z)
• Mind the Gap! Static and Interactive Evaluations of Large Audio Models [55.87220295533817]
  Large Audio Models (LAMs) are designed to power voice-native experiences.<n>This study introduces an interactive approach to evaluate LAMs and collect 7,500 LAM interactions from 484 participants.
  arXiv  Detail & Related papers  (2025-02-21T20:29:02Z)
• Beyond Sample-Level Feedback: Using Reference-Level Feedback to Guide Data Synthesis [54.15152681093108]
  We introduce Reference-Level Feedback, a paradigm that extracts desirable characteristics from carefully curated reference samples to guide the synthesis of higher-quality instruction-response pairs.<n>Experiments demonstrate that Reference-Level Feedback consistently outperforms traditional sample-level feedback methods, generalizes across model architectures, and produces high-quality and diverse data at low cost.
  arXiv  Detail & Related papers  (2025-02-06T21:29:00Z)
• A Systematic Examination of Preference Learning through the Lens of Instruction-Following [83.71180850955679]
  We use a novel synthetic data generation pipeline to generate 48,000 instruction unique-following prompts.<n>With our synthetic prompts, we use two preference dataset curation methods - rejection sampling (RS) and Monte Carlo Tree Search (MCTS)<n>Experiments reveal that shared prefixes in preference pairs, as generated by MCTS, provide marginal but consistent improvements.<n>High-contrast preference pairs generally outperform low-contrast pairs; however, combining both often yields the best performance.
  arXiv  Detail & Related papers  (2024-12-18T15:38:39Z)
• Offline Regularised Reinforcement Learning for Large Language Models Alignment [33.483481840098925]
  We propose DRO, or emphDirect Reward optimisation, as a framework and associated algorithms. DRO uses a simple mean-squared objective that can be implemented in various ways.
  arXiv  Detail & Related papers  (2024-05-29T14:11:29Z)
• Revisiting the Evaluation of Image Synthesis with GANs [55.72247435112475]
  This study presents an empirical investigation into the evaluation of synthesis performance, with generative adversarial networks (GANs) as a representative of generative models. In particular, we make in-depth analyses of various factors, including how to represent a data point in the representation space, how to calculate a fair distance using selected samples, and how many instances to use from each set.
  arXiv  Detail & Related papers  (2023-04-04T17:54:32Z)
• A Revised Generative Evaluation of Visual Dialogue [80.17353102854405]
  We propose a revised evaluation scheme for the VisDial dataset. We measure consensus between answers generated by the model and a set of relevant answers. We release these sets and code for the revised evaluation scheme as DenseVisDial.
  arXiv  Detail & Related papers  (2020-04-20T13:26:45Z)

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.