Kavli Affiliate: Yi Zhou
| First 5 Authors: Rui Yang, Li Fang, Yi Zhou, ,
| Summary:
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}.
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