| Challenge: | Existing methods on diagram generation with LLMs rely heavily on proprietary LLM systems. |
| Approach: | They propose a new evaluation metric to assess demonstration diagrams generated by large language models. |
| Outcome: | The proposed evaluation metric evaluates diagrams produced by state-of-the-art LLMs on recent research literature. |
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Yu Wang, Ryan A. Rossi, Namyong Park, Nesreen K. Ahmed, Danai Koutra, Franck Dernoncourt, Tyler Derr
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GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets (2025.findings-naacl)
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DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)
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Can LLMs Convert Graphs to Text-Attributed Graphs? (2025.naacl-long)
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