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.
@article{arxiv.2405.17249,
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}
}
Comments
Accepted at 18th International Conference on Neural-Symbolic Learning and Reasoning, NESY 2024. Evaluating Mixtral-8x7b-Instruct-v0.1, GPT-3.5-Turbo-0125 and GPT-4o for Knowledge Graph Completion task with prompts formatted according to the TELeR taxonomy