Summary of Applying Fine-tuned Llms For Reducing Data Needs in Load Profile Analysis, by Yi Hu et al.
Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile Analysis
by Yi Hu, Hyeonjin Kim, Kai Ye, Ning Lu
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method utilizes fine-tuned Large Language Models (LLMs) to minimize data requirements in load profile analysis, effectively restoring missing data in power system load profiles. The two-stage fine-tuning strategy adapts a pre-trained LLM, GPT-3.5, for missing data restoration tasks. Empirical evaluation demonstrates the effectiveness of the fine-tuned model, achieving comparable performance to state-of-the-art models like BERT-PIN. Key findings highlight prompt engineering and optimal fine-tuning samples, showcasing few-shot learning’s efficiency in transferring knowledge from general user cases to specific target users. The approach shows notable cost-effectiveness and time efficiency compared to training models from scratch, making it a practical solution for scenarios with limited data availability and computing resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to use language models to fix missing data in power grid load profiles. This method is very good at restoring missing data and is comparable to other methods that were specifically designed for this task. The key parts of the method are making sure the prompt engineering is correct and using the right fine-tuning samples. This approach also shows that it’s more efficient than training models from scratch, which makes it useful for situations where there isn’t a lot of data or computing power. |
Keywords
» Artificial intelligence » Bert » Few shot » Fine tuning » Gpt » Prompt
