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
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning
Dongmin Park, Zhaofang Qian, Guangxing Han, Ser-Nam Lim
Intent-conditioned and Non-toxic Counterspeech Generation using Multi-Task Instruction Tuning with RLAIF
Amey Hengle, Aswini Kumar, Sahajpreet Singh, Anil Bandhakavi, Md Shad Akhtar, Tanmoy Chakroborty
ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses
Chang Zong, Yuyan Chen, Weiming Lu, Jian Shao, Yongfeng Huang, Heng Chang, Yueting Zhuang
ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
Ahmed Masry, Mehrad Shahmohammadi, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty
MuseGraph: Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining
Yanchao Tan, Hang Lv, Xinyi Huang, Jiawei Zhang, Shiping Wang, Carl Yang
LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation
Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Guojun Peng, Zhiguang Cao, Yining Ma, Yue-Jiao Gong