NeurIPS 2024 Competition LMC Submissions

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
- Published: 12 Dec 2024, Last Modified: 12 Dec 2024
- LMC 2024 Oral
- Readers: Everyone
Model Merging using Geometric Median of Task Vectors
Siddharth Gupta, Aakash Gupta
- Published: 12 Dec 2024, Last Modified: 12 Dec 2024
- LMC 2024 Oral
- Readers: Everyone
Interpolated Layer-Wise Merging for NeurIPS 2024 LLM Merging Competition
Rio Akizuki, Nozomu Yoshinari, Yuya Kudo, Yoichi Hirose, Toshiyuki Nishimoto, Kento Uchida, Shinichi Shirakawa
- Published: 12 Dec 2024, Last Modified: 12 Dec 2024
- LMC 2024 Oral
- Readers: Everyone
A Model Merging Method
Jisheng Fang, Hao Mo, QiangGao
- Published: 12 Dec 2024, Last Modified: 22 Mar 2025
- LMC 2024 Oral
- Readers: Everyone
Differentiable DARE-TIES for NeurIPS 2024 LLM Merging Competition
Toshiyuki Nishimoto, Yoichi Hirose, Yuya Kudo, Nozomu Yoshinari, Rio Akizuki, Kento Uchida, Shinichi Shirakawa
- Published: 12 Dec 2024, Last Modified: 12 Dec 2024
- LMC 2024 Oral
- Readers: Everyone
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
- Published: 12 Dec 2024, Last Modified: 12 Dec 2024
- LMC 2024 Oral
- Readers: Everyone
Simple Llama Merge: What Kind of LLM Do We Need?
Yinuo Zhang
- Published: 12 Dec 2024, Last Modified: 12 Dec 2024
- LMC 2024 Oral
- Readers: Everyone
LLM Merging Competition Technical Report for NeurIPS 2024: Efficiently Building Large Language Models through Merging
Yizhen Zhang, Yang Ding, Jie Wu, Yujiu Yang
- Published: 12 Dec 2024, Last Modified: 12 Dec 2024
- LMC 2024 Oral
- Readers: Everyone
MoD: A Distribution-Based Approach for Merging Large Language Models
Quy-Anh Dang, Chris Ngo
- Published: 12 Dec 2024, Last Modified: 12 Dec 2024
- LMC 2024 Oral
- Readers: Everyone