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 this http URL 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.
Abstract (translated)
本文介绍了PolyPersona，这是一个用于跨多个领域生成条件化人物的调查回应的生成框架。该框架使用参数高效的LoRA适配器和4位量化技术，在资源自适应训练设置下对紧凑型聊天模型进行指令微调。一个基于对话的数据管道明确保留了人物线索，确保生成的回答在行为上保持一致性。利用这个数据管道，我们构建了一个包含3,568个合成调查回应的数据集，这些回应涵盖了十个领域和433种不同的角色，这使得控制性指令微调和系统性的跨域评估成为可能。 通过一个多指标评估套件对生成的响应进行了评估，该套件结合了标准文本生成度量（如BLEU、ROUGE和BERTScore）与针对调查设计的特定度量，旨在评估结构连贯性、风格一致性以及情感准确性。实验结果表明，像TinyLlama 1.1B和Phi-2这样的紧凑模型在性能上可媲美7B至8B规模的基础线模型，最高BLEU得分为0.090，ROUGE-1为0.429。 这些发现证明了人物条件化微调使小型语言模型能够生成可靠且连贯的合成调查数据。所提出的框架提供了一种高效且可重复的方法来生成调查数据，并支持大规模评估同时通过透明和开放协议促进偏差分析。
URL
https://arxiv.org/abs/2512.14562
