Summary of Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?, by Yifan Feng et al.
Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?
by Yifan Feng, Chengwu Yang, Xingliang Hou, Shaoyi Du, Shihui Ying, Zongze Wu, Yue Gao
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The paper introduces LLM4Hypergraph, a comprehensive benchmark for evaluating large language models (LLMs) on hypergraphs. Unlike existing benchmarks that focus on pairwise relationships, LLM4Hypergraph includes problems that test high-order correlations found in real-world data. The benchmark consists of 21,500 problems across eight low-order, five high-order, and two isomorphism tasks, using both synthetic and real-world hypergraphs from citation networks and protein structures. Six prominent LLMs are evaluated, including GPT-4o, demonstrating the effectiveness of the benchmark in identifying model strengths and weaknesses. The paper also proposes specialized prompting frameworks, Hyper-BAG and Hyper-COT, which enhance high-order reasoning and achieve an average 4% (up to 9%) performance improvement on structure classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs can process large amounts of data, but they often struggle with understanding complex relationships between entities. To address this limitation, researchers created a new benchmark called LLM4Hypergraph. This benchmark includes many different types of problems that test an LLM’s ability to understand high-order correlations in real-world data. The benchmark is made up of 21,500 problems that use synthetic and real-world data from fields like biology and computer science. Six popular LLMs were tested using the new benchmark, which helps identify their strengths and weaknesses. |
Keywords
» Artificial intelligence » Classification » Gpt » Prompting
