| Autor: |
Wang, Xin, Chen, Zirui, Wang, Haofen, U, Leong Hou, Li, Zhao, Guo, Wenbin |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
The integration of Large Language Models (LLM) with Knowledge Representation Learning (KRL) signifies a significant advancement in the field of artificial intelligence (AI), enhancing the ability to capture and utilize both structure and textual information. Despite the increasing research on enhancing KRL with LLMs, a thorough survey that analyse processes of these enhanced models is conspicuously absent. Our survey addresses this by categorizing these models based on three distinct Transformer architectures, and by analyzing experimental data from various KRL downstream tasks to evaluate the strengths and weaknesses of each approach. Finally, we identify and explore potential future research directions in this emerging yet underexplored domain. |
| Databáze: |
arXiv |
| Externí odkaz: |
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