Summary of Reasoning on Efficient Knowledge Paths:knowledge Graph Guides Large Language Model For Domain Question Answering, by Yuqi Wang et al.
Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering
by Yuqi Wang, Boran Jiang, Yi Luo, Dawei He, Peng Cheng, Liangcai Gao
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 Medium Difficulty Summary: Large language models (LLMs) like GPT3.5, GPT4, and LLAMA2 have achieved impressive performance on various tasks, even surpassing human experts in some cases. However, they often struggle with hallucination problems due to inadequate training data. Fine-tuning large models can be challenging, especially when dealing with open-source limitations or constructing high-quality domain instruction. In this context, structured knowledge databases like knowledge graphs (KGs) can better provide domain background knowledge for LLMs and leverage their reasoning and analysis capabilities more effectively. The authors propose a pipeline for selecting reasoning paths from KG based on LLMs, which reduces the dependency on these models. Additionally, they introduce a simple and effective subgraph retrieval method based on chain of thought (CoT) and page rank, returning the most likely answer-containing paths. Experimental results on three datasets, including GenMedGPT-5k, WebQuestions, and CMCQA, demonstrate that using fewer LLM calls can achieve comparable performance to previous state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper is about how we can make large language models work better. These models are like super smart computers that can understand and generate human-like text. They’re really good at some tasks, but sometimes they make mistakes because they weren’t trained well enough. The authors want to find a way to help these models by giving them more information about the topic they’re talking about. They use something called a knowledge graph, which is like a big database of facts and ideas. This helps the model understand what it’s talking about and make better decisions. The authors also come up with a new way to search for answers using this knowledge graph, which works really well. They test their method on three different datasets and show that it can do just as well as other methods that use more of these language models. |
Keywords
» Artificial intelligence » Fine tuning » Hallucination » Knowledge graph
