Update README.md

README.md

@@ -82,9 +82,9 @@ Large Language Models (LLMs) have shown remarkable progress in natural language
 - (*EMNLP'22*) Language Models of Code are Few-Shot Commonsense Learners [[paper](https://arxiv.org/abs/2210.07128)][[code](https://github.com/reasoning-machines/CoCoGen)]
 - (*SIGIR'23*) Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction [[paper](https://arxiv.org/abs/2210.10709)][[code](https://github.com/zjunlp/RAP)]
 - (*TKDE‘23*) AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models [[paper](https://arxiv.org/abs/2307.11772)][[code](https://github.com/ruizhang-ai/AutoAlign)]
+- (*AAAI'24*) Graph Neural Prompting with Large Language Models [[paper](https://arxiv.org/abs/2309.15427)][[code](https://github.com/meettyj/GNP)]
 - (*arXiv 2023.04*) CodeKGC: Code Language Model for Generative Knowledge Graph Construction [[paper](https://arxiv.org/abs/2304.09048)][[code](https://github.com/zjunlp/DeepKE/tree/main/example/llm/CodeKGC)]
 - (*arXiv 2023.05*) Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs [[paper](https://arxiv.org/abs/2305.09858)]
-- (*arXiv 2023.09*) Graph Neural Prompting with Large Language Models [[paper](https://arxiv.org/abs/2309.15427)]
 - (*arXiv 2023.10*) Faithful Path Language Modelling for Explainable Recommendation over Knowledge Graph [[paper](https://arxiv.org/abs/2310.16452)]
 - (*arXiv 2023.10*) Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning [[paper](https://arxiv.org/abs/2310.01061)][[code](https://github.com/RManLuo/reasoning-on-graphs)]
 - (*arXiv 2023.11*) Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models [[paper](https://arxiv.org/abs/2311.10112)]