Update README.md

README.md

@@ -31,11 +31,28 @@ python inference.py --load_8bit --base_model 'meta-llama/Llama-2-7b-hf' --lora_w
 If you find our work helpful, please consider [citing][paper] the following papers.
 
 ```bibtex
-@article{shi2023llama,
-  title={LLaMA-E: Empowering E-commerce Authoring with Multi-Aspect Instruction Following},
-  author={Shi, Kaize and Sun, Xueyao and Wang, Dingxian and Fu, Yinlin and Xu, Guandong and Li, Qing},
-  journal={arXiv preprint arXiv:2308.04913},
-  year={2023}
+@inproceedings{shi-etal-2025-llama,
+    title = "{LL}a{MA}-{E}: Empowering {E}-commerce Authoring with Object-Interleaved Instruction Following",
+    author = "Shi, Kaize  and
+      Sun, Xueyao  and
+      Wang, Dingxian  and
+      Fu, Yinlin  and
+      Xu, Guandong  and
+      Li, Qing",
+    editor = "Rambow, Owen  and
+      Wanner, Leo  and
+      Apidianaki, Marianna  and
+      Al-Khalifa, Hend  and
+      Eugenio, Barbara Di  and
+      Schockaert, Steven",
+    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
+    month = jan,
+    year = "2025",
+    address = "Abu Dhabi, UAE",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.coling-main.58/",
+    pages = "870--885",
+    abstract = "E-commerce authoring entails creating engaging, diverse, and targeted content to enhance preference elicitation and retrieval experience. While Large Language Models (LLMs) have revolutionized content generation, they often fall short in e-commerce applications due to their limited memorization of domain-specific features. This paper proposes LLaMA-E, the unified e-commerce authoring models that address the contextual preferences of customers, sellers, and platforms, the essential objects in e-commerce operation. We design the instruction set derived from tasks of ads generation, query-enhanced product title rewriting, product classification, purchase intent speculation, and general e-commerce Q{\&}A. The instruction formulation ensures the interleaved cover of the presented and required object features, allowing the alignment of base models to parameterize e-commerce knowledge comprehensively. The proposed LLaMA-E models achieve state-of-the-art evaluation performance and exhibit the advantage in zero-shot practical applications. To our knowledge, this is the first LLM tailored to empower authoring applications with comprehensive scenario understanding by integrating features focused on participated objects."
 }
 ```