Bi-Directional Multi-Granularity Generation Framework for Knowledge Graph-to-Text with Large Language Model (2024.acl-short)
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| Challenge: | Existing methods generate whole text based on all KG triples at once and may incorporate incorrect KG Triples for each sentence. |
| Approach: | They propose a bi-directional multi-granularity generation framework that generates graph-level sentences based on KG triples instead of the whole text at a time. |
| Outcome: | The proposed framework achieves state-of-the-art in benchmark dataset WebNLG and further analysis shows the efficiency of different modules. |
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