RedditPersona Framework Adapts LLMs Using 16M Comments Across 112 Subreddits

Researchers (Ghaffari et al.) introduced RedditPersona, a modular framework designed to standardize the process of adapting large language models to specific online communities sourced from Reddit. The framework addresses the lack of comparability across existing studies by providing a unified pipeline for data collection, community definition, model training, and evaluation. It collects posts and comments from 112 subreddits in the urban well-being domain, profiling 301,429 active users with over 16 million comments. Users are partitioned under five grouping strategies: subreddit-based, graph-structural (using user interaction graphs), semantic (via content embeddings), hybrid, and interaction-based. Each strategy is then used to train a parameter-efficient adapter for the base LLM using QLoRA, ensuring lightweight customization.

The evaluation suite measures fluency, fidelity (how well the adapter reflects the community's style), distributional alignment with real text, and community identifiability (how easily a classifier can detect which community produced the text). Applied to the chosen subreddits, the study found that the identifiability of each adapter closely tracks the intrinsic agreement of its grouping strategy with the subreddit baseline. Crucially, a consistent trade-off emerged across all five strategies: adapters that are more easily identifiable as belonging to a specific community tend to have lower distributional similarity to real human text. The code and configuration files are publicly available, enabling researchers to reproduce and extend the work, thus improving reproducibility in community-conditioned LLM research.