Accepted Papers for NeurIPS 2024 Competition LMC Submission

1. MoD: A Distribution-Based Approach for Merging Large Language Models
   Quy-Anh Dang, Chris Ngo

2. LLM Merging Competition Technical Report for NeurIPS 2024: Efficiently Building Large Language Models through Merging
   Yizhen Zhang, Yang Ding, Jie Wu, Yujiu Yang

3. Simple Llama Merge: What Kind of LLM Do We Need?
   Yinuo Zhang

4. LLM Merging Competition Technical Report: Efficient Model Merging with Strategic Model Selection, Merging, and Hyperparameter Optimization
   Zixiang Di, Yaoming Yang, Mei Jiang, Bingdong Li, Hong Qian, Aimin Zhou

5. Differentiable DARE-TIES for NeurIPS 2024 LLM Merging Competition
   Toshiyuki Nishimoto, Yoichi Hirose, Yuya Kudo, Nozomu Yoshinari, Rio Akizuki, Kento Uchida, Shinichi Shirakawa

6. A Model Merging Method
   QiangGao, Jisheng Fang, Hao Mo

7. Interpolated Layer-Wise Merging for NeurIPS 2024 LLM Merging Competition
   Rio Akizuki, Nozomu Yoshinari, Yuya Kudo, Yoichi Hirose, Toshiyuki Nishimoto, Kento Uchida, Shinichi Shirakawa

8. Model Merging using Geometric Median of Task Vectors
   Siddharth Gupta, Aakash Gupta

9. Towards an approach combining Knowledge Graphs and Prompt Engineering for Merging Large Language Models
   Furel De Consol TEGUIMENE YENDJI, Azanzi Jiomekong, Sanju Tiwari, Carick Appolinaire ATEZONG YMELE, Janice Anta Zebaze

Qualified participants will be invited to write a report.

• We will select winning groups for the top three positions: 1st place, 2nd place, and 3rd place ¹.
  • The first place winning group will receive a $8,000 cash prize,
  • The second place group will receive $3,000, and
  • The third place group will receive $2,000.

• Additionally, we will present one 1K award for the most interesting finding awarded by the organizing committee and 1K prize for a GPU budget-friendly submission.

• The winning teams will be invited to make a presentation at NeurIPS conference.

1. _{The cash prize policy may subject to the guidance from US OFAC (Office of Foreign Assets Control).}  ↩