Text-based knowledge graph completion (KGC) methods, leveraging textual entity descriptions are at the research forefront. The efficacy of these models hinges on the quality of the textual data. This study explores whether enriched or more efficient textual descriptions can amplify model performance. Recently, Large Language Models (LLMs) have shown remarkable improvements in NLP tasks, attributed to their sophisticated text generation and conversational capabilities. LLMs assimilate linguistic patterns and integrate knowledge from their training data. Compared to traditional databases like Wikipedia, LLMs provide several advantages, facilitating broader information querying and content augmentation. We hypothesize that LLMs, without fine-tuning, can refine entity descriptions, serving as an auxiliary knowledge source. An in-depth analysis was conducted to verify this hypothesis. We found that (1) without fine-tuning, LLMs have the capability to further improve the quality of entity text descriptions. We validated this through experiments on the FB15K-237 and WN18RR datasets. (2) LLMs exhibit text generation hallucination issues and selectively output words with multiple meanings. This was mitigated by contextualizing prompts to constrain LLM outputs. (3) Larger model sizes do not necessarily guarantee better performance; even the 7B model can achieve optimized results in this comparative task. These findings underscore the untapped potential of large models in text-based KGC, which is a promising direction for further research in KGC. The code and datasets are accessible at \href{https://github.com/sjlmg/CP-KGC}.

翻译：文本知识图谱补全方法利用文本实体描述，处于研究前沿。这些模型的效果取决于文本数据的质量。本研究探讨了更丰富或更高效的文本描述能否提升模型性能。近期，大语言模型在自然语言处理任务中展现出显著进步，这归功于其复杂的文本生成和对话能力。大语言模型能够吸收语言模式，并从训练数据中整合知识。与传统数据库（如维基百科）相比，大语言模型具备多项优势，有助于更广泛的信息查询和内容增强。我们假设，无需微调，大语言模型即可优化实体描述，充当辅助知识源。我们进行了深入分析以验证这一假设。我们发现：（1）无需微调，大语言模型能够进一步提升实体文本描述的质量。我们通过在FB15K-237和WN18RR数据集上的实验验证了这一点。（2）大语言模型存在文本生成幻觉问题，并会选择性输出带有多义义的词汇。我们通过上下文提示约束大语言模型输出来缓解这一问题。（3）更大的模型规模并不必然带来更优性能；即使是7B模型也能在此比较任务中取得优化结果。这些发现凸显了大模型在文本知识图谱补全中的未开发潜力，这是知识图谱补全领域一个颇具前景的研究方向。代码和数据集可在\href{https://github.com/sjlmg/CP-KGC}获取。