构建标准化框架，让大模型更好适配Reddit社区风格
RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit
- 按五种策略划分社区，用轻量适配器训练模型
- 112个社区数据验证：社区识别度与真实文本相似度存在稳定权衡
- 代码开源，可复现，适合研究社区化语言模型的学者
社区化语言模型适配需在数据收集、社区定义和评估方式上做选择，但当前研究各自为政，难以比较或复用。我们提出RedditPersona，一个模块化框架：从Reddit收集帖子与评论，分析301,429个用户画像，基于五个分组策略（子版块、图结构、语义、混合、互动）划分社区，每策略使用QLoRA训练参数高效适配器，并采用统一指标体系评估流畅性、保真度、分布对齐与社区可识别性。在城市福祉领域112个子版块上的实验表明，适配器的行为识别度与其策略与子版块基准的一致性正相关，且所有策略均呈现识别度与真实文本分布相似性之间的稳定权衡。代码与配置文件已公开。
原文摘要 · Abstract (English)
Community-conditioned language model adaptation needs choices about data collection, community definition, and evaluation that are currently made independently in each study, making it hard to compare assumptions or reuse artifacts. We present RedditPersona, a modular framework that standardizes these choices: it collects Reddit posts and comments, profiles active users, partitions them under five grouping strategies (subreddit-based, graph-structural, semantic, hybrid, and interaction-based), trains a parameter-efficient adapter per strategy via QLoRA, and evaluates them under a shared metric suite spanning fluency, fidelity, distributional alignment, and community identifiability. Applied to 112 subreddits in the urban well-being domain (301,429 user profiles, 16M+ comments), we find that adapters' behavioral identifiability tracks each strategy's agreement with the subreddit baseline, and that a consistent trade-off between identifiability and distributional similarity to real text holds across all five strategies. The code and configuration files are available at: https://github.com/Ahghaffari/redditpersona.
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