Few-Shot Learning Meets Large Language Models: Mining Medicine Interventions from Reddit

Abstract

Extracting structured information from noisy social media discussions remains a significant challenge due to unstandardized language, data sparsity, and the need for precise alignment of entities such as pharmaceutical terms. Traditional Natural Language Processing pipelines often require extensive preprocessing and task-specific annotations, limiting their scalability. In this work, we propose a novel Large Language Model (LLM) framework based on instruction following few-shot learning to extract pharmaceutical interventions from Reddit discussions. Our framework leverages structured prompting to generate schema-constrained outputs, reducing post-processing complexity and enabling seamless normalization to biomedical ontologies. This design bridges the flexibility of Question Answering-style extraction (no predefined entity lists required) with the precision of structured approaches such as named entity recognition. In this study, we analyze Reddit discussions from the “r/covid19positive” subreddit, spanning multiple phases of the pandemic, to evaluate our method. We further benchmark three state-of-the-art LLMs — GPT-3.5 Turbo, Gemini 1.5 Flash, and GPT-4o-Mini — highlighting their respective strengths and weaknesses in few-shot settings. Results demonstrate that our framework provides an effective and scalable solution for mining complex, user-generated text.

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