Summary of Can Llms Perform Structured Graph Reasoning?, by Palaash Agrawal et al.
Can LLMs perform structured graph reasoning?
by Palaash Agrawal, Shavak Vasania, Cheston Tan
First submitted to arxiv on: 2 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 Medium Difficulty Summary: Pretrained Large Language Models (LLMs) have shown impressive reasoning abilities through language-based prompts, but often struggle with structured tasks due to input representation limitations. To address this, researchers designed graph reasoning tasks as a proxy for semi-structured tasks, testing the ability of various LLMs (GPT-4, GPT-3.5, Claude-2, Llama-2, and Palm-2) to navigate complex representations beyond plain text. The study analyzed model performance across different settings, highlighting limitations, biases, and properties of LLMs. Notably, a new prompting technique called PathCompare demonstrated improved performance in graph traversal tasks compared to standard Chain-of-Thought (CoT). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This research is about how well big language models can solve puzzles that involve complex patterns and structures. The models are usually very good at understanding natural language, but they struggle with problems that require structured thinking. To test their abilities, the researchers created a series of graph-based puzzles that get progressively harder. They then used five different language models to see which one could solve these puzzles best. The study found that the models have some limitations and biases, but also showed promise when given special guidance on how to approach these types of problems. |
Keywords
» Artificial intelligence » Claude » Gpt » Llama » Palm » Prompting
