## 5 Discussion and Implications
### Discussion on the Results by Specification Type  
Analysis based on the intended use of the data shows clear strengths and weaknesses in each model. The LLaMA2-13B model generally outperformed the Davinci-002 model, particularly in accuracy and handling complex content. However, the Davinci-002 model's lower frequency of receiving the lowest scores in certain categories indicates areas where it maintains robust performance.  
* Anchor bolt (Design-Related Numerical Values): The Davinci-002 model scored an average of 2.26, while the LLaMA2-13B model scored 3.86. This indicates the LLaMA2-13B model's superior ability in accurately providing design-related numerical values.
* Corrosion allow (Textual Content Accuracy): The average score for the LLaMA2-13B model was higher at 3.16 compared to 2.88 for the Davinci-002 model. However, it is noteworthy that the Davinci-002 model received fewer lowest scores (1 point)-5 times compared to 16 times for the LLaMA2-13B model.
* Welding seam (Text + Number Contents): Here, the Davinci-002 model scored an average of 2.64, while the LLaMA2-13B model scored 3.64, indicating better performance of the LLaMA2-13B model in handling combined text and numeric content.
* Positive material identification (Referencing Accuracy): Both models scored high (Davinci-002 model: 3.94, LLaMA2-13B model: 4.36). The high scores in this category can be attributed to the relatively simpler and clearer reference document numbers used in training, which leave little room for creative interpretation by the generative LLMs.  
Hallucination, in the context of artificial intelligence, refers to the phenomenon where an AI model produces plausible but incorrect answers or errors in information processing, similar to illusions or delusions. This can occur when the AI model produces results based on information that is not present in its training data, leading to results that may be coherent but factually incorrect, or even biased and of low relevance. Hallucinations in AI are often observed when the available data is scarce or incomplete. Generative LLMs can hallucinate, generating factually incorrect or unrealistic information. This issue, which arises from limitations in training data, inherent design, prompt ambiguity, and lack of real-world understanding, is critical in evaluating model responses. The hallucination tendency of the fine-tuned completion for a category is judged by the number of times the scores 1 and 2 were received. This is because if the post fine-tuning models include content other than 'Search for other related specification,' they will receive a low score. Among a total of 45 datasets corresponding to 'Search for other related specifications,' the Davinci-002 model recorded a score of 2 or less 18 times in the 'Anchor bolt' type, 13 times in the 'Corrosion allowance' type, and 14 times in the 'Welding seam' type. And in the case of the LLaMA2-13B model, the scores 1, 5, and 2 were observed in the order of 'Anchor bolt,' 'Corrosion allowance,' and 'Welding seam,', respectively. Thus, the hallucination tendency can be judged to be greater in the Davinci-002 model than in the LLaMA2-13B model.