Summary of Debate on Graph: a Flexible and Reliable Reasoning Framework For Large Language Models, by Jie Ma et al.
Debate on Graph: a Flexible and Reliable Reasoning Framework for Large Language Models
by Jie Ma, Zhitao Gao, Qi Chai, Wangchun Sun, Pinghui Wang, Hongbin Pei, Jing Tao, Lingyun Song, Jun Liu, Chen Zhang, Lizhen Cui
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an iterative interactive Knowledge Graph Question Answering (KGQA) framework that leverages the interactive learning capabilities of Large Language Models (LLMs) to perform reasoning and Debating over Graphs (DoG). The framework, DoG, employs a subgraph-focusing mechanism to mitigate the impact of lengthy reasoning paths and utilizes a multi-role debate team to gradually simplify complex questions. Experimental results on five public datasets demonstrate the effectiveness and superiority of our architecture, outperforming state-of-the-art method ToG by 23.7% and 9.1% in accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how computers can answer natural language questions using large amounts of information stored in knowledge graphs. The problem is that current methods have two big challenges: they get distracted from the answer because they reason too much, or they provide wrong answers because they don’t know what’s relevant. To fix this, the authors propose a new approach called DoG, which uses machines to debate and narrow down the possible answers. This helps them find the right answer faster and more accurately. The results show that DoG does a better job than other methods, especially on complex questions. |
Keywords
» Artificial intelligence » Knowledge graph » Question answering
