Parameter-efficient finetuning in large language models for efficient business data summarization

Tóm tắt

Text summarization in business domain is an inevitable and pivotal task when considering data driven decision-making procedure. Creating succinct and contextual aware summaries has become crucial for effective information consumption due to the increasing volume and complexity of business reports, proposals, and financial documents. The main goal of this study is to use parameter-efficient fine-tuning techniques to leverage large language models (LLMs) for business data summarization. To balance performance and computational cost, the study specifically used the LLaMA 3 model that refined using the QLoRA methodology. The suggested method guarantees excellent summaries that are accurate and pertinent to business contexts by integrating domain-specific knowledge and utilizing effective adaptation techniques. The efficacy of the model is validated through evaluation using common metrics such as ROUGE and BLEU. The findings show how optimized LLMs can improve strategic decision-making and business intelligence.