Summary of Filter-then-generate: Large Language Models with Structure-text Adapter For Knowledge Graph Completion, by Ben Liu et al.
Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion
by Ben Liu, Jihai Zhang, Fangquan Lin, Cheng Yang, Min Peng
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper focuses on bridging the gap between Large Language Models (LLMs) and knowledge graph completion (KGC). Despite LLMs’ impressive performance in natural language processing, they consistently underperform conventional KGC approaches. The authors propose a novel instruction-tuning-based method, FtG, to address the challenges of entity candidates, hallucinations, and graph structure exploitation. FtG uses a filter-then-generate paradigm, formulating the KGC task as multiple-choice questions. Experimental results show that FtG achieves significant performance gains compared to existing state-of-the-art methods. The paper’s contributions include a flexible ego-graph serialization prompt, a structure-text adapter, and an instruction dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to fill in missing information about people, places, or things. This is called knowledge graph completion (KGC). Large Language Models are great at some tasks, but they’re not good at KGC. Researchers want to help them do better. They came up with a new way to teach LLMs using questions and answers. This method, called FtG, helps the models avoid making things up and understand the relationships between different pieces of information. The results show that FtG works much better than other methods. Now we can use LLMs to help us learn more about the world. |
Keywords
» Artificial intelligence » Instruction tuning » Knowledge graph » Natural language processing » Prompt
