Summary of Multi-hop Question Answering Over Knowledge Graphs Using Large Language Models, by Abir Chakraborty
Multi-hop Question Answering over Knowledge Graphs using Large Language Models
by Abir Chakraborty
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Databases (cs.DB)
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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 research paper explores knowledge graphs (KGs), large datasets with specific structures representing large knowledge bases. The authors investigate how language models (LLMs) can answer questions over KGs that involve multiple hops. They evaluate the capability of LLMs to extract relevant information from KGs and feed it into their fixed context window, achieving competitive performance on six KGs. The paper highlights the importance of considering the size and nature of the KG when selecting an approach for answering questions, with both semantic parsing (SP) and information-retrieval based methods showing promise. The authors’ evaluation demonstrates the potential of LLMs to reason over multiple edges in a KG, enabling effective question answering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a big bookshelf filled with lots of books about different topics. This research is about finding ways for computers to answer questions by searching through this “bookshelf” (called a knowledge graph). The authors want to know how good language models are at finding answers when they need to jump from one book to another on the shelf. They tested these language models on six different sets of books and found that they can do a great job, especially if they use two different ways to search through the books: either by following specific paths or by looking for key words. This research helps us understand how computers can better answer questions by searching through large amounts of information. |
Keywords
» Artificial intelligence » Context window » Knowledge graph » Question answering » Semantic parsing
