Summary of Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective, by Bo Ni et al.
Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective
by Bo Ni, Yu Wang, Lu Cheng, Erik Blasch, Tyler Derr
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 Recently, Knowledge Graphs (KGs) have been combined with Large Language Models (LLMs) to improve their reasoning capabilities. However, these frameworks lack rigorous uncertainty estimation, making them unreliable for high-stakes applications. To address this gap, we propose Uncertainty Aware Knowledge-Graph Reasoning (UAG), a new trustworthy KG-LLM framework that incorporates uncertainty quantification. UAG features an uncertainty-aware multi-step reasoning framework using conformal prediction, providing theoretical guarantees on predictions. Additionally, an error rate control module adjusts the error rate within individual components. Our experiments show UAG can achieve any pre-defined coverage rate while reducing prediction set/interval size by 40% compared to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure computers are good at giving answers and being honest about how good they are. Right now, some systems that use lots of information to answer questions aren’t very good at telling us when they’re not sure. The researchers came up with a new way to make these systems more trustworthy by adding a special feature that helps them be more accurate. They tested this new system and found it could give answers quickly while also being honest about its uncertainty. |
Keywords
» Artificial intelligence » Knowledge graph
