Summary of Infuserki: Enhancing Large Language Models with Knowledge Graphs Via Infuser-guided Knowledge Integration, by Fali Wang et al.
InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration
by Fali Wang, Runxue Bao, Suhang Wang, Wenchao Yu, Yanchi Liu, Wei Cheng, Haifeng Chen
First submitted to arxiv on: 18 Feb 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 proposed framework, {method}, addresses the issue of efficiently integrating unknown knowledge into Large Language Models (LLMs) without sacrificing existing knowledge. By leveraging transformer internal states to determine when to enrich LLM outputs, this innovative approach effectively prevents knowledge forgetting. Compared to state-of-the-art baselines, {method} successfully integrates new knowledge and outperforms them on UMLS-2.5k and MetaQA domain knowledge graphs, reducing knowledge forgetting by 9% and 6%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a way to teach Large Language Models (LLMs) new things without losing what they already know. It’s like building upon existing knowledge instead of starting from scratch. The authors created a special framework that uses the internal workings of LLMs to decide when to add more information, so they don’t forget what they already learned. The results show that this approach is better than previous methods at adding new knowledge without losing old knowledge. |
Keywords
* Artificial intelligence * Transformer
