Poster "instruction tuning" Papers
19 papers found
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
Rui Ye, Jingyi Chai, Xiangrui Liu et al.
Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning
Fanrui Zhang, Dian Li, Qiang Zhang et al.
Fine-tuning with Reserved Majority for Noise Reduction
Shuyang Jiang, Yusheng Liao, Ya Zhang et al.
HMVLM:Human Motion-Vision-Language Model via MoE LoRA
Lei Hu, Yongjing Ye, Shihong Xia
Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning
Simran Kaur, Simon Park, Anirudh Goyal et al.
Online Video Understanding: OVBench and VideoChat-Online
Zhenpeng Huang, Xinhao Li, Jiaqi Li et al.
Scaling Instruction-tuned LLMs to Million-token Contexts via Hierarchical Synthetic Data Generation
Linda He, Jue Wang, Maurice Weber et al.
A Closer Look at the Limitations of Instruction Tuning
Sreyan Ghosh, Chandra Kiran Evuru, Sonal Kumar et al.
Dolphins: Multimodal Language Model for Driving
Yingzi Ma, Yulong Cao, Jiachen Sun et al.
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data
Peng, Xinyi Ling, Ziru Chen et al.
Evaluating Model Bias Requires Characterizing its Mistakes
Isabela Albuquerque, Jessica Schrouff, David Warde-Farley et al.
Executable Code Actions Elicit Better LLM Agents
Xingyao Wang, Yangyi Chen, Lifan Yuan et al.
Instruction Tuning for Secure Code Generation
Jingxuan He, Mark Vero, Gabriela Krasnopolska et al.
InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining
Boxin Wang, Wei Ping, Lawrence McAfee et al.
LESS: Selecting Influential Data for Targeted Instruction Tuning
Mengzhou Xia, Sadhika Malladi, Suchin Gururangan et al.
MathScale: Scaling Instruction Tuning for Mathematical Reasoning
Zhengyang Tang, Xingxing Zhang, Benyou Wang et al.
Parameter-Efficient Fine-Tuning with Discrete Fourier Transform
Ziqi Gao, Qichao Wang, Aochuan Chen et al.
Thermometer: Towards Universal Calibration for Large Language Models
Maohao Shen, Subhro Das, Kristjan Greenewald et al.
ULTRAFEEDBACK: Boosting Language Models with Scaled AI Feedback
Ganqu Cui, Lifan Yuan, Ning Ding et al.