Instruction Tuning
Instruction tuning refines large language models (LLMs) by training them on datasets of instructions and desired responses, improving their ability to follow diverse commands and generate helpful outputs. Current research emphasizes improving data quality and diversity through techniques like data partitioning, synthetic data generation, and novel prompting strategies, often applied to various model architectures including LLMs and multimodal models. This area is significant because it directly addresses the limitations of pre-trained LLMs, leading to safer, more reliable, and more useful AI systems across numerous applications, from chatbots to specialized tools for medical diagnosis and remote sensing.
Papers
VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models
Jiawei Liang, Siyuan Liang, Man Luo, Aishan Liu, Dongchen Han, Ee-Chien Chang, Xiaochun Cao
Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions?
Alexander Arno Weber, Klaudia Thellmann, Jan Ebert, Nicolas Flores-Herr, Jens Lehmann, Michael Fromm, Mehdi Ali
Learning to Poison Large Language Models During Instruction Tuning
Yao Qiang, Xiangyu Zhou, Saleh Zare Zade, Mohammad Amin Roshani, Prashant Khanduri, Douglas Zytko, Dongxiao Zhu
A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion
Yanzhen Shen, Yu Zhang, Yunyi Zhang, Jiawei Han
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Haoran Li, Qingxiu Dong, Zhengyang Tang, Chaojun Wang, Xingxing Zhang, Haoyang Huang, Shaohan Huang, Xiaolong Huang, Zeqiang Huang, Dongdong Zhang, Yuxian Gu, Xin Cheng, Xun Wang, Si-Qing Chen, Li Dong, Wei Lu, Zhifang Sui, Benyou Wang, Wai Lam, Furu Wei
Thermometer: Towards Universal Calibration for Large Language Models
Maohao Shen, Subhro Das, Kristjan Greenewald, Prasanna Sattigeri, Gregory Wornell, Soumya Ghosh
LLM can Achieve Self-Regulation via Hyperparameter Aware Generation
Siyin Wang, Shimin Li, Tianxiang Sun, Jinlan Fu, Qinyuan Cheng, Jiasheng Ye, Junjie Ye, Xipeng Qiu, Xuanjing Huang
Contrastive Instruction Tuning
Tianyi Lorena Yan, Fei Wang, James Y. Huang, Wenxuan Zhou, Fan Yin, Aram Galstyan, Wenpeng Yin, Muhao Chen
Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models
Zihao Lin, Mohammad Beigi, Hongxuan Li, Yufan Zhou, Yuxiang Zhang, Qifan Wang, Wenpeng Yin, Lifu Huang
Instruction Diversity Drives Generalization To Unseen Tasks
Dylan Zhang, Justin Wang, Francois Charton
Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models
Dheeraj Mekala, Alex Nguyen, Jingbo Shang
Instruction Tuning for Secure Code Generation
Jingxuan He, Mark Vero, Gabriela Krasnopolska, Martin Vechev
DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
Yejie Wang, Keqing He, Guanting Dong, Pei Wang, Weihao Zeng, Muxi Diao, Yutao Mou, Mengdi Zhang, Jingang Wang, Xunliang Cai, Weiran Xu