"instruction fine-tuning" Papers
12 papers found
Conference
AdaGrad under Anisotropic Smoothness
Yuxing Liu, Rui Pan, Tong Zhang
ICLR 2025arXiv:2406.15244
14
citations
Ensembles of Low-Rank Expert Adapters
Yinghao Li, Vianne Gao, Chao Zhang et al.
ICLR 2025arXiv:2502.00089
6
citations
Is In-Context Learning Sufficient for Instruction Following in LLMs?
Hao Zhao, Maksym Andriushchenko, francesco croce et al.
ICLR 2025arXiv:2405.19874
22
citations
Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models
Lucas Bandarkar, Benjamin Muller, Pritish Yuvraj et al.
ICLR 2025arXiv:2410.01335
15
citations
Making Large Vision Language Models to Be Good Few-Shot Learners
Fan Liu, Wenwen Cai, Jian Huo et al.
AAAI 2025paperarXiv:2408.11297
6
citations
Rethinking the Role of Verbatim Memorization in LLM Privacy
Tom Sander, Bargav Jayaraman, Mark Ibrahim et al.
NEURIPS 2025
VCM: Vision Concept Modeling with Adaptive Vision Token Compression via Instruction Fine-Tuning
Run Luo, Renke Shan, Longze Chen et al.
NEURIPS 2025
Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding
Yan Shu, Zheng Liu, Peitian Zhang et al.
CVPR 2025arXiv:2409.14485
155
citations
Whose Instructions Count? Resolving Preference Bias in Instruction Fine-Tuning
Jiayu Zhang, Changbang Li, Yinan Peng et al.
NEURIPS 2025
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning
Hao Zhao, Maksym Andriushchenko, Francesco Croce et al.
ICML 2024arXiv:2402.04833
88
citations
Physics of Language Models: Part 3.1, Knowledge Storage and Extraction
Zeyuan Allen-Zhu, Yuanzhi Li
ICML 2024spotlightarXiv:2309.14316
244
citations
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models
Yongshuo Zong, Ondrej Bohdal, Tingyang Yu et al.
ICML 2024arXiv:2402.02207
123
citations