Abstract:

To address the issues of poor question answering （QA） performance due to the lack of high-quality datasets in the multi-energy power generation field， as well as the current limitations in the generalization capability of Chinese responses， a fine-tuned large language model called EcoPowerGPT based on the Llama architecture was proposed for the multi-energy power generation field. By organizing literature and reports in the multi-energy power generation field， the model employed classification filtering and multi-dimensional scoring methods for data processing， thereby constructing a fine-tuned dataset for multi-energy power generation. This dataset was then used to fine-tune the large language model. Comparative experiments were conducted between EcoPowerGPT and six other dialogue models on multi-energy power generation QA test sets and test sets of multiple-choice questions with a single correct answer. The results demonstrate that EcoPowerGPT outperforms existing dialogue models in terms of both the accuracy and comprehensiveness of its responses.

Key words: generative large language model, question answering, natural language processing, multi-energy-power generation, instruction fine-tuning