Summary of Harnessing the Power Of Large Language Model For Uncertainty Aware Graph Processing, by Zhenyu Qian et al.
Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing
by Zhenyu Qian, Yiming Qian, Yuting Song, Fei Gao, Hai Jin, Chen Yu, Xia Xie
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 A novel approach is introduced for handling large and complex graph data, combining a large language model (LLM) with an uncertainty-aware module to provide interpretable explanations. The proposed method achieves state-of-the-art results on two tasks: few-shot knowledge graph completion and graph classification, surpassing existing algorithms by a substantial margin across ten diverse benchmark datasets. The approach is parameter-efficient and fine-tuned through LLM, leveraging its capacity to process graph data effectively. Additionally, the uncertainty estimation based on perturbation and calibration scheme are proposed to quantify the confidence scores of generated answers, demonstrating an AUC of 0.8 or higher in predicting answer correctness for seven out of ten datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to work with big graph data. Right now, there are two main approaches: one that relies on assumptions and another that uses deep learning, but both have limitations. The new method combines the power of a large language model (LLM) with an uncertainty-aware module to provide answers that make sense. It works really well for tasks like filling in missing information in a knowledge graph and classifying graphs. The results are impressive, beating existing methods on ten different datasets. Additionally, the paper proposes ways to measure how confident we should be in the answers generated by the LLM. |
Keywords
* Artificial intelligence * Auc * Classification * Deep learning * Few shot * Knowledge graph * Large language model * Parameter efficient
