Update README.md

README.md

@@ -55,13 +55,24 @@ The three configurations (**full-hints, no-hints, one-try**) in our paper differ
 
 ## 📖 Citation
 ```
-@misc{lopez2024nytconnectionsdeceptivelysimpletext,
-      title={NYT-Connections: A Deceptively Simple Text Classification Task that Stumps System-1 Thinkers}, 
-      author={Angel Yahir Loredo Lopez and Tyler McDonald and Ali Emami},
-      year={2024},
-      eprint={2412.01621},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2412.01621}, 
+@inproceedings{loredo-lopez-etal-2025-nyt,
+    title = "{NYT}-Connections: A Deceptively Simple Text Classification Task that Stumps System-1 Thinkers",
+    author = "Loredo Lopez, Angel Yahir  and
+      McDonald, Tyler  and
+      Emami, Ali",
+    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.134/",
+    pages = "1952--1963",
+    abstract = "Large Language Models (LLMs) have shown impressive performance on various benchmarks, yet their ability to engage in deliberate reasoning remains questionable. We present NYT-Connections, a collection of 358 simple word classification puzzles derived from the New York Times Connections game. This benchmark is designed to penalize quick, intuitive {\textquotedblleft}System 1{\textquotedblright} thinking, isolating fundamental reasoning skills. We evaluated six recent LLMs, a simple machine learning heuristic, and humans across three configurations: single-attempt, multiple attempts without hints, and multiple attempts with contextual hints. Our findings reveal a significant performance gap: even top-performing LLMs like GPT-4 fall short of human performance by nearly 30{\%}. Notably, advanced prompting techniques such as Chain-of-Thought and Self-Consistency show diminishing returns as task difficulty increases. NYT-Connections uniquely combines linguistic isolation, resistance to intuitive shortcuts, and regular updates to mitigate data leakage, offering a novel tool for assessing LLM reasoning capabilities."
 }
 ```