Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via
Alignment Tax Reduction

Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via Alignment Tax Reduction

22 May 2024

Rui Yan

Papers citing "Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via Alignment Tax Reduction"

8 / 8 papers shown

Title
On the Loss of Context-awareness in General Instruction Fine-tuning Yihan Wang Andrew Bai Nanyun Peng Cho-Jui Hsieh 100 1 0 05 Nov 2024
Focus On This, Not That! Steering LLMs With Adaptive Feature Specification Tom A. Lamb Adam Davies Alasdair Paren Philip Torr Francesco Pinto 47 0 0 30 Oct 2024
Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models Jinliang Lu Ziliang Pang Min Xiao Yaochen Zhu Rui Xia Jiajun Zhang MoMe 49 18 0 08 Jul 2024
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 59 14 0 24 Jun 2024
Cascade Reward Sampling for Efficient Decoding-Time Alignment Bolian Li Yifan Wang A. Grama Ruqi Zhang Ruqi Zhang AI4TS 49 9 0 24 Jun 2024
Reasons to Reject? Aligning Language Models with Judgments Weiwen Xu Deng Cai Zhisong Zhang Wai Lam Shuming Shi ALM 21 14 0 22 Dec 2023
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 322 11,953 0 04 Mar 2022
Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models Tianlu Wang Rohit Sridhar Diyi Yang Xuezhi Wang AAML 120 72 0 14 Oct 2021