Опубликована: Янв. 1, 2024

This study proposes a hybrid method using two types of large language models (LLM) for prompt response to defect complaints by exploring rapidly potential causes and repair methods via machine reading comprehension (MRC) tasks. Although numerous past maintenance records guidelines offer valuable insights into or newly reported defects, manually reviewing all data is impractical due the significant time effort required. MRC natural processing (NLP) task that trains read extensive texts answer questions. While recent state-of-the-art (SOTA) LLMs, as they are, exhibit high performance general questions, falter in specialized domains require fine-tuning. However, generating question-answer (QA) datasets fine-tuning time-consuming, taking over 200 days with crowdsourcing. Furthermore, many companies restrict LLM usage daily tasks leakage risks. To mitigate these challenges, this introduces approach wherein Bidirectional Encoder Representations from Transformers (BERT) fine-tuned QA datasets, automatically generated Generative Pre-trained Transformer (GPT) publicly available construction guidelines. The GPT-applied part proposed 2,548 pairs seven half hours, significantly reducing dataset generation time. For MRC, BERT achieved competitive highest F1 score 88.0%, outperforming Korean benchmark's (68.5%). contributes reduced cost resources constructing domain-specific performing efficient complaint within data-secure environment.

Язык: Английский