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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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Measuring and Improving the Efficiency of Python Code Generated by LLMs Using CoT Prompting and Fine-Tuning
The burgeoning sophistication of Artificial Intelligence (AI) has catalyzed the rapid proliferation of Large Language Models (LLMs) within software development. These models are increasingly employed to automate the generation of functionally correct code, address complex computational problems, and facilitate the debugging of existing software systems. However, LLM-generated code often faces challenges due to inherent inefficiencies, including redundant logical structures, factually inconsistent content (hallucinations), and programming errors. To address this issue, our research rigorously evaluated the computational efficiency of Python code generated by three prominent LLMs: GPT-4o-Mini, GPT-3.5-Turbo, and GPT-4-Turbo. The evaluation metrics encompass execution time, memory utilization, and peak memory consumption, while maintaining the functional correctness of the generated code. Leveraging the EffiBench benchmark datasets within the Google Vertex AI Workbench environment, across a spectrum of machine configurations, the study implemented a consistent seed parameter to ensure experimental reproducibility. Furthermore, we investigated the impact of two distinct optimization strategies: Chain-of-Thought (CoT) prompting and model fine-tuning. Our findings reveal a significant enhancement in efficiency metrics for GPT-4o-Mini and GPT-3.5-Turbo when employing CoT prompting; however, this trend was not observed for GPT-4-Turbo. Based on its promising performance with CoT prompting, we selected the GPT-4o-Mini model for subsequent fine-tuning, aiming to further enhance both its computational efficiency and accuracy. However, contrary to our expectations, fine-tuning the GPT-4o-Mini model led to a discernible degradation in both its accuracy and computational efficiency. In conclusion, this study provides empirical evidence suggesting that the deployment of high-CPU machine configurations, in synergy with the utilization of the GPT-4o-Mini model and CoT prompting techniques, yields demonstrably more efficient and accurate LLM-generated Python code, particularly within computationally intensive application scenarios.
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- Award ID(s):
- 2334243
- PAR ID:
- 10621809
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Access
- Volume:
- 13
- ISSN:
- 2169-3536
- Page Range / eLocation ID:
- 119657 to 119681
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1109/ACCESS.2025.3585742
