Text summarization is a critical Natural Language Processing (NLP) task with applications ranging from information retrieval to content generation. Leveraging Large Language Models (LLMs) has shown remarkable promise in enhancing summarization techniques. This paper embarks on an exploration of text summarization with a diverse set of LLMs, including MPT-7b-instruct, falcon-7b-instruct, and OpenAI ChatGPT text-davinci-003 models. The experiment was performed with different hyperparameters and evaluated the generated summaries using widely accepted metrics such as the Bilingual Evaluation Understudy (BLEU) Score, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) Score, and Bidirectional Encoder Representations from Transformers (BERT) Score. According to the experiment, text-davinci-003 outperformed the others. This investigation involved two distinct datasets: CNN Daily Mail and XSum. Its primary objective was to provide a comprehensive understanding of the performance of Large Language Models (LLMs) when applied to different datasets. The assessment of these models' effectiveness contributes valuable insights to researchers and practitioners within the NLP domain. This work serves as a resource for those interested in harnessing the potential of LLMs for text summarization and lays the foundation for the development of advanced Generative AI applications aimed at addressing a wide spectrum of business challenges.

这篇研究论文使用了各种不同的大型语言模型，包括MPT-7b-instruct，falcon-7b-instruct和OpenAI ChatGPT text-davinci-003模型，通过不同的超参数对生成的摘要进行评估，并发现text-davinci-003模型的表现优于其他模型。该研究还分析了CNN Daily Mail和XSum两个不同的数据集，旨在提供对大型语言模型在不同数据集上应用时性能的全面理解。这项工作为对NLP领域的研究人员和从业者提供了有价值的见解，同时也为开发应对各种业务挑战的高级生成式人工智能应用奠定了基础。

大语言模型的文本摘要：MPT-7b-instruct、Falcon-7b-instruct和OpenAI Chat-GPT模型的比较研究