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Collections including paper arxiv:2306.01708

Model Merging is a very popular technique nowadays in LLM. Here is a chronological list of papers on the space that will help you get started with it!

Qualitatively characterizing neural network optimization problems

Paper • 1412.6544 • Published Dec 19, 2014 • 4
Convergent Learning: Do different neural networks learn the same representations?

Paper • 1511.07543 • Published Nov 24, 2015 • 2
Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models

Paper • 1909.11299 • Published Sep 25, 2019 • 1
Model Fusion via Optimal Transport

Paper • 1910.05653 • Published Oct 12, 2019 • 1

Model Merging Papers

Collection of relevant papers about model merging

Qualitatively characterizing neural network optimization problems

Paper • 1412.6544 • Published Dec 19, 2014 • 4
Averaging Weights Leads to Wider Optima and Better Generalization

Paper • 1803.05407 • Published Mar 14, 2018 • 2
Merging Models with Fisher-Weighted Averaging

Paper • 2111.09832 • Published Nov 18, 2021 • 1
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 5

Foundation AI Papers

Curated List of Must-Reads on LLM reasoning at Temus AI team

Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Paper • 2310.04406 • Published Oct 6, 2023 • 8
Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 90
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization

Paper • 2402.09320 • Published Feb 14 • 6
Self-Discover: Large Language Models Self-Compose Reasoning Structures

Paper • 2402.03620 • Published Feb 6 • 102

Dynamics of Transformer Language Model Features

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 5
Diverse Weight Averaging for Out-of-Distribution Generalization

Paper • 2205.09739 • Published May 19, 2022 • 1
Fusing finetuned models for better pretraining

Paper • 2204.03044 • Published Apr 6, 2022 • 5
Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs

Paper • 2309.07311 • Published Sep 13, 2023 • 2

Papers about model merging

referenced in the mergekit repo: https://github.com/cg123/mergekit

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 5
Editing Models with Task Arithmetic

Paper • 2212.04089 • Published Dec 8, 2022 • 4
Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 10
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

Paper • 2311.03099 • Published Nov 6, 2023 • 27

Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Paper • 2308.06093 • Published Aug 11, 2023 • 2
Platypus: Quick, Cheap, and Powerful Refinement of LLMs

Paper • 2308.07317 • Published Aug 14, 2023 • 22
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers

Paper • 2211.11315 • Published Nov 21, 2022 • 1
LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Paper • 2307.13269 • Published Jul 25, 2023 • 29

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