The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
End-to-End Graph Flattening Method for Large Language Models
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“End-to-End DAG-Path (EEDP) prompting [Hong et al., 2024] addresses the limitations of traditional graph-flattening methods, such as adjacency lists and edge lists, which struggle with long-distance reasoning in graph-related tasks for LLMs.”
From A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications (2024) · cites this paper as Hong et al., 2024
In the citing paper
“EEDP's key insight is that conventional flattened representations often lose critical long-range dependencies essential for effective reasoning.”
From A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications (2024) · cites this paper as Hong et al., 2024
In the citing paper
“EEDP was evaluated on tasks such as Edge Prediction Connectivity Prediction (EPCP) and Edge Prediction Distance Prediction (EPDP) using educational (Merged_1000) and molecular (ZINC_test_2500) datasets.”
From A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications (2024) · cites this paper as Hong et al., 2024
In the citing paper
“To mitigate this, EEDP prioritizes the main backbone paths connecting graph endpoints (nodes with zero in-degree or out-degree) while preserving adjacency lists to maintain local contextual information.”
From A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications (2024) · cites this paper as Hong et al., 2024
In the citing paper
“The evaluation results highlighted significant performance gains over traditional baselines, with EPCP showing a +10.21% accuracy improvement on Merged_1000 and +16.76% on ZINC_test_2500.”
From A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications (2024) · cites this paper as Hong et al., 2024
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Citations
Cites
A Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Dong, Z., Hou, Y., Zhang, B., Min, Y., Zhang, J., Liu, P., et al. (2026) · Frontiers of Computer Science · doi:10.1007/s11704-026-60308-3 · Cited by 2,093
Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs
Chen, Z., Mao, H., Li, H., Jin, W., Wen, H., Wei, X., Wang, S., Yin, D., Fan, W., Liu, H., et al. (2024) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3655103.3655110 · Cited by 209
Self-citationTalk like a Graph: Encoding Graphs for Large Language Models
Fatemi, B., Halcrow, J., & Perozzi, B. (2023) · arXiv · doi:10.48550/arxiv.2310.04560 · Cited by 17
Research works printed but not linked: 16
- 10.1145/3404835.3463028 · DOI
- 10.1145/3404835.3462929 · DOI
- 10.18653/v1/2023.findings-acl.358 · DOI
- 10.18653/v1/2023.findings-acl.553 · DOI
- 10.1145/3442381.3450112 · DOI
- 10.1016/j.aiopen.2024.02.001 · DOI
- 10.1145/3477495.3531984 · DOI
- 10.18653/v1/D19-1629 · DOI
- 10.18653/v1/2022.emnlp-main.90 · DOI
- Wang, 2024, Can language models solve graph problems in natural language?, Advances in Neural Information Processing Systems, 36
- 10.24963/ijcai.2024/898 · DOI
- Himsolt, 1997, Gml: Graph modelling language
- 10.1201/b15385-19 · doi:10.1201/b15385-19
- 10.1017/CBO9780511750854 · DOI · Paper page
- 10.1145/2939672.2939754 · DOI · Paper page
- Hamilton, 2017, Inductive representation learning on large graphs, Advances in neural information processing systems, 30
Cited by
A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
Sahoo, P., Singh, A. K., Saha, S., Jain, V., Mondal, S., & Chadha, A. (2024) · arXiv · doi:10.48550/arxiv.2402.07927 · Cited by 288
Metadata
- Authors
- Hong, B., Wu, J., Liu, J., Ding, L., Sha, J., Zhang, K., Wang, S., & Huang, Z.
- Affiliations
- University of Science and Technology of China,State Key Laboratory of Cognitive Intelligence,Hefei,China
- iFLYTEK Research, iFLYTEK Co., Ltd,Hefei,China
- iFLYTEK Co., Ltd,iFLYTEK AI Research (Central China),Hefei,China
- Venue
- 2024 International Conference on Computational Linguistics and Natural Language Processing (CLNLP)
- Publisher
- IEEE
- Published
- 2024-07-19
- Pages
- 41-45
- Cited by
- 0
- References
- 3
- Funders
- Research and Development
- Fundamental Research Funds for the Central Universities
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- CoChat. (2026). Citation check: End-to-End Graph Flattening Method for Large Language Models. https://app.cochat.ai/papers/10.1109/clnlp64123.2024.00016
- The paper
- Hong, B., Wu, J., Liu, J., Ding, L., Sha, J., Zhang, K., Wang, S., & Huang, Z. (2024). End-to-End Graph Flattening Method for Large Language Models. 2024 International Conference on Computational Linguistics and Natural Language Processing (CLNLP). https://doi.org/10.1109/clnlp64123.2024.00016
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
