Summary of Codegraph: Enhancing Graph Reasoning Of Llms with Code, by Qiaolong Cai et al.
CodeGraph: Enhancing Graph Reasoning of LLMs with Code
by Qiaolong Cai, Zhaowei Wang, Shizhe Diao, James Kwok, Yangqiu Song
First submitted to arxiv on: 25 Aug 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 The proposed method, CodeGraph, is an innovative solution for assessing large language models’ (LLMs) abilities in processing complex graph reasoning tasks. By encoding graph problem solutions as code, CodeGraph learns from exemplars, generates programs, and executes them via a program interpreter. In this paper, the authors evaluate CodeGraph using four different LLMs: GPT-3.5 Turbo, Llama3-70B Instruct, Mixtral-8x22B Instruct, and Mixtral-8x7B Instruct. The experimental results on six graph encoding methods in the GraphQA dataset demonstrate that CodeGraph can improve performance by 1.3% to 58.6%, depending on the task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CodeGraph is a new way to help computers understand and solve graph problems. This is important because big language models are getting better at understanding text, but they struggle with complex math problems. Current methods try to turn graphs into words that these models can understand, but this doesn’t always work well. CodeGraph takes a different approach by turning the answers into code that the model can execute. In this study, researchers tested CodeGraph using four different big language models and found that it improved their performance on graph reasoning tasks. |
Keywords
» Artificial intelligence » Gpt
