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Linear Interpolation In Parameter Space is Good Enough for Fine-Tuned
  Language Models

Linear Interpolation In Parameter Space is Good Enough for Fine-Tuned Language Models

22 November 2022
Mark Rofin
Nikita Balagansky
Daniil Gavrilov
    MoMe
    KELM
ArXivPDFHTML

Papers citing "Linear Interpolation In Parameter Space is Good Enough for Fine-Tuned Language Models"

10 / 10 papers shown
Title
How to Merge Your Multimodal Models Over Time?
How to Merge Your Multimodal Models Over Time?
Sebastian Dziadzio
Vishaal Udandarao
Karsten Roth
Ameya Prabhu
Zeynep Akata
Samuel Albanie
Matthias Bethge
MoMe
98
2
0
09 Dec 2024
Merge to Learn: Efficiently Adding Skills to Language Models with Model
  Merging
Merge to Learn: Efficiently Adding Skills to Language Models with Model Merging
Jacob Morrison
Noah A. Smith
Hannaneh Hajishirzi
Pang Wei Koh
Jesse Dodge
Pradeep Dasigi
KELM
MoMe
CLL
36
1
0
16 Oct 2024
WARP: On the Benefits of Weight Averaged Rewarded Policies
WARP: On the Benefits of Weight Averaged Rewarded Policies
Alexandre Ramé
Johan Ferret
Nino Vieillard
Robert Dadashi
Léonard Hussenot
Pierre-Louis Cedoz
Pier Giuseppe Sessa
Sertan Girgin
Arthur Douillard
Olivier Bachem
54
13
0
24 Jun 2024
Learn Your Reference Model for Real Good Alignment
Learn Your Reference Model for Real Good Alignment
Alexey Gorbatovski
Boris Shaposhnikov
Alexey Malakhov
Nikita Surnachev
Yaroslav Aksenov
Ian Maksimov
Nikita Balagansky
Daniil Gavrilov
OffRL
52
26
0
15 Apr 2024
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive
  Learning
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning
Hao Zhang
Nianwen Si
Yaqi Chen
Wenlin Zhang
Xukui Yang
Dan Qu
Weiqiang Zhang
35
9
0
20 Apr 2023
Git Re-Basin: Merging Models modulo Permutation Symmetries
Git Re-Basin: Merging Models modulo Permutation Symmetries
Samuel K. Ainsworth
J. Hayase
S. Srinivasa
MoMe
252
313
0
11 Sep 2022
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally
  Across Scales and Tasks
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
Xiao Liu
Kaixuan Ji
Yicheng Fu
Weng Lam Tam
Zhengxiao Du
Zhilin Yang
Jie Tang
VLM
238
805
0
14 Oct 2021
Analyzing Monotonic Linear Interpolation in Neural Network Loss
  Landscapes
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
James Lucas
Juhan Bae
Michael Ruogu Zhang
Stanislav Fort
R. Zemel
Roger C. Grosse
MoMe
154
28
0
22 Apr 2021
The Power of Scale for Parameter-Efficient Prompt Tuning
The Power of Scale for Parameter-Efficient Prompt Tuning
Brian Lester
Rami Al-Rfou
Noah Constant
VPVLM
280
3,844
0
18 Apr 2021
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language
  Understanding
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Alex Jinpeng Wang
Amanpreet Singh
Julian Michael
Felix Hill
Omer Levy
Samuel R. Bowman
ELM
297
6,950
0
20 Apr 2018
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