Summary of An Enhanced Prompt-based Llm Reasoning Scheme Via Knowledge Graph-integrated Collaboration, by Yihao Li et al.
An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaboration
by Yihao Li, Ru Zhang, Jianyi Liu
First submitted to arxiv on: 7 Feb 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 This paper proposes a novel collaborative training-free reasoning scheme that combines Knowledge Graph (KG) and Large Language Models (LLMs) to overcome limitations in practical applications. The scheme iteratively explores KG using LLMs to selectively retrieve task-relevant knowledge subgraphs, and then guides the LLMs to combine implicit knowledge and reason on the subgraph while explicitly elucidating the reasoning process. This cooperative approach achieves more reliable knowledge-based reasoning and facilitates tracing of results. Experimental results show significant improvements across multiple datasets, including a 10% improvement on the QALD10 dataset compared to state-of-the-art works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve problems with Large Language Models (LLMs) by combining them with Knowledge Graphs (KG). LLMs are good at some tasks but have issues like making things up and not being clear about how they got their answers. The new approach uses KG to help LLMs find the right information, then lets the LLM reason on it while explaining its thought process. This makes the results more reliable and easier to understand. The test results show that this approach is better than previous methods on many datasets. |
Keywords
» Artificial intelligence » Knowledge graph
