Update citation and add tech report (#317)

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README.md

@@ -52,7 +52,7 @@
 
 <details>
   <summary>Latest News 🔥</summary>
-
+  - [2024/10/21] We have released the tech report of Liger Kernel on Arxiv: https://arxiv.org/pdf/2410.10989 
   - [2024/9/6] We release v0.2.1 ([X post](https://x.com/liger_kernel/status/1832168197002510649)). 2500+ Stars, 10+ New Contributors, 50+ PRs, 50k Downloads in two weeks!
   - [2024/8/31] CUDA MODE talk, [Liger-Kernel: Real-world Triton kernel for LLM Training](https://youtu.be/gWble4FreV4?si=dxPeIchhkJ36Mbns), [Slides](https://github.com/cuda-mode/lectures?tab=readme-ov-file#lecture-28-liger-kernel)
   - [2024/8/23] Official release: check out our [X post](https://x.com/hsu_byron/status/1827072737673982056)
@@ -358,11 +358,15 @@ It also includes components from projects licensed under:
 
 Biblatex entry:
 ```bib
-@software{liger2024,
-  title  = {Liger-Kernel: Efficient Triton Kernels for LLM Training},
-  author = {Hsu, Pin-Lun and Dai, Yun and Kothapalli, Vignesh and Song, Qingquan and Tang, Shao and Zhu, Siyu},
-  url    = {https://github.com/linkedin/Liger-Kernel},
-  year   = {2024}
+@article{hsu2024ligerkernelefficienttriton,
+      title={Liger Kernel: Efficient Triton Kernels for LLM Training}, 
+      author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},
+      year={2024},
+      eprint={2410.10989},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2410.10989},
+      journal={arXiv preprint arXiv:2410.10989},
 }
 ```