Experiment design and implementation of knowledge data generation based on large models

In the era of artificial intelligence (AI), large language models (LLMs) have demonstrated exceptional capabilities in natural language understanding and generation. However, two critical bottlenecks persist in their practical application: hallucination and the scarcity of high-quality domain-specific data. To address these challenges, this study designs a comprehensive experimental scheme for knowledge-to-text data generation. By leveraging the structured constraints of knowledge graphs, we transform triple-based knowledge into fluent and accurate natural language descriptions. This research provides a reliable, reproducible technical approach for improving the factual consistency of LLMs and expanding high-quality training corpora in specialized domains.

The study performs end-to-end LLM fine-tuning using a curated dataset of 14231 triple-text pairs. The experimental methodology comprises several rigorous stages. First, data preprocessing is conducted. Knowledge triples (subjects, predicates, and objects) are serialized into specific input sequences to adapt to the model’s vocabulary. The dataset is partitioned into training and test sets of 7398 and 6833 samples, respectively. Second, we use BART and T5-PEGASUS as the primary generation models, with GPT for comparative analysis. A distributed fine-tuning framework is implemented using torch.distributed and Distributed Data Parallel (DDP) to leverage multi-GPU computing resources. To optimize computational efficiency and reduce memory overhead, NVIDIA Apex is integrated for FP16 mixed-precision training. The training objective is guided by the cross-entropy loss function to minimize the discrepancy between the predicted sequences and the ground truth. Throughout the process, a real-time monitoring mechanism based on terminal logs tracks loss fluctuations and performance metrics. Finally, the model is evaluated using ROUGE-L, BLEU, chrF++, and BERTScore, providing a comprehensive assessment of both linguistic overlap and semantic similarity.

The experimental results are as follows: (1) Models based on the encoder–decoder architecture outperform traditional decoder-only architectures in terms of structural-to-text transformation. (2) The BART model achieves a ROUGE-L score of 0.3435 and a BERTScore of 0.8872. Preserving the structural integrity of input triples achieves an optimal balance between information coverage and linguistic fluency. (3) Despite achieving a lower ROUGE-L of 0.1350 due to its concise generation style, the T5-PEGASUS model attains a high BERTScore of 0.8710, indicating superior semantic accuracy in short-text generation. (4) Real-time monitoring logs show that the implementation of DDP and mixed-precision training enables the training loss to converge from 0.1698 to 0.1238 within five epochs, demonstrating the efficiency of the proposed training framework. (5) Qualitative analysis of the generated samples confirms that the models can accurately synthesize multiple complex triples into coherent sentences while preserving key factual entities.

This study validates the effectiveness of fine-tuning pretrained encoder–decoder models with structured knowledge constraints for high-quality data generation. By integrating distributed training techniques and a rigorous evaluation framework, the proposed scheme overcomes the limitations of manual data annotation and mitigates the hallucination issues inherent in LLMs. In the optimized fine-tuning process, targeted loss function minimization and real-time monitoring ensure that the generated knowledge data is semantically rich and factually reliable. The proposed experimental paradigm for knowledge engineering and large-scale model optimization offers significant practical value for industrial AI deployment.