Summary of Finetuning Generative Large Language Models with Discrimination Instructions For Knowledge Graph Completion, by Yang Liu and Xiaobin Tian and Zequn Sun and Wei Hu
Finetuning Generative Large Language Models with Discrimination Instructions for Knowledge Graph Completion
by Yang Liu, Xiaobin Tian, Zequn Sun, Wei Hu
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a finetuning framework called DIFT that enables large language models (LLMs) to complete knowledge graphs (KGs) in a text-generation manner while avoiding grounding errors. The framework uses a lightweight model to obtain candidate entities and then finetunes the LLM with discrimination instructions to select the correct one from the given candidates. To improve performance, DIFT employs a truncated sampling method to select useful facts for finetuning and injects KG embeddings into the LLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way for computers to complete information in knowledge graphs using large language models. Instead of just guessing answers, this approach uses a special training process to help the model make more accurate predictions. The technique is tested on several datasets and shows promising results. |
Keywords
» Artificial intelligence » Grounding » Text generation
