Unlock the Power of Frozen LLMs in Knowledge Graph Completion
<|reference_start|>Unlock the Power of Frozen LLMs in Knowledge Graph Completion: Traditional knowledge graph completion (KGC) methods rely solely on structural information, struggling with the inherent sparsity of knowledge graphs (KGs). Large Language Models (LLMs) learn extensive knowledge from large corpora with powerful context modeling, making them promising for mitigating the limitations of previous methods. Directly fine-tuning LLMs offers great capability but comes at the cost of huge time and memory consumption, while utilizing frozen LLMs yields suboptimal results.In this work, we aim to leverage LLMs for KGC effectively and efficiently. We capture the context-aware hidden states of knowledge triples by employing prompts to stimulate the intermediate layers of LLMs. We then train a data-efficient classifier on these hidden states to harness the inherent capabilities of frozen LLMs in KGC. Additionally, to reduce ambiguity and enrich knowledge representation, we generate detailed entity descriptions through subgraph sampling on KGs. Extensive experiments on standard benchmarks demonstrate the efficiency and effectiveness of our approach. We outperform traditional KGC methods across most datasets and, notably, achieve classification performance comparable to fine-tuned LLMs while enhancing GPU memory efficiency by $188\times$ and accelerating training and inference by $13.48\times$.<|reference_end|>
@article{xue2024unlock,
    title={Unlock the Power of Frozen LLMs in Knowledge Graph Completion},
    author={Bo Xue, Yi Xu, Yunchong Song, Yiming Pang, Yuyang Ren, Jiaxin Ding,
  Luoyi Fu, Xinbing Wang},
    journal={arXiv preprint arXiv:2408.06787},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06787},
    primaryClass={cs.CL}
}