Papers with ego-graph serialization prompt

1 papers
Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion (2025.coling-main)

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Challenge: Empirical evidence suggests that LLMs perform worse than conventional KGC approaches.
Approach: They propose a filter-then-generate paradigm and a multiple-choice question format to harness the capability of LLMs while mitigating the issue casused by hallucinations.
Outcome: The proposed method achieves substantial performance gain compared to existing state-of-the-art methods.

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