Assessing LLMs Suitability for Knowledge Graph Completion
<|reference_start|>Assessing LLMs Suitability for Knowledge Graph Completion: Recent work has shown the capability of Large Language Models (LLMs) to solve tasks related to Knowledge Graphs, such as Knowledge Graph Completion, even in Zero- or Few-Shot paradigms. However, they are known to hallucinate answers, or output results in a non-deterministic manner, thus leading to wrongly reasoned responses, even if they satisfy the user's demands. To highlight opportunities and challenges in knowledge graphs-related tasks, we experiment with three distinguished LLMs, namely Mixtral-8x7b-Instruct-v0.1, GPT-3.5-Turbo-0125 and GPT-4o, on Knowledge Graph Completion for static knowledge graphs, using prompts constructed following the TELeR taxonomy, in Zero- and One-Shot contexts, on a Task-Oriented Dialogue system use case. When evaluated using both strict and flexible metrics measurement manners, our results show that LLMs could be fit for such a task if prompts encapsulate sufficient information and relevant examples.<|reference_end|>
@article{iga2024assessing,
    title={Assessing LLMs Suitability for Knowledge Graph Completion},
    author={Vasile Ionut Remus Iga and Gheorghe Cosmin Silaghi},
    journal={arXiv preprint arXiv:2405.17249},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17249},
    primaryClass={cs.CL cs.AI}
}