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RedditPersona introduces a standardized, modular framework for community-conditioned LLM adaptation that resolves fragmentation in data collection, user profiling, and evaluation metrics. By applying parameter-efficient QLoRA adapters across five distinct grouping strategies, the system demonstrates a consistent trade-off between behavioral identifiability and distributional similarity to real text. This work provides a reusable artifact suite for researchers aiming to systematically compare assumptions and improve the fidelity of specialized language models.
RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit
arXiv:2606.06027v1 Announce Type: new Abstract: Community-conditioned language model adaptation requires 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 intrinsic 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.
Abstract:Community-conditioned language model adaptation requires 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 intrinsic 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: this https URL.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
arXiv:2606.06027 [cs.AI]
(or arXiv:2606.06027v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.06027 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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