instruction tuning
A technique used in training AI models, particularly language models, where the model is fine-tuned on a set of instructions and corresponding outputs to improve its ability to follow human-like prompts and generate appropriate responses in varied contexts.
- Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark
- Chain of Execution Supervision Promotes General Reasoning in Large Language Models
- CoIDO: Efficient Data Selection for Visual Instruction Tuning via Coupled Importance-Diversity Optimization
- Efficient Data Selection at Scale via Influence Distillation
- From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning
- INST-IT: Boosting Instance Understanding via Explicit Visual Prompt Instruction Tuning
- Pixel Reasoner: Incentivizing Pixel Space Reasoning via Curiosity-Driven Reinforcement Learning
- T-SHIRT: Token-Selective Hierarchical Data Selection for Instruction Tuning
- Task-Specific Data Selection for Instruction Tuning via Monosemantic Neuronal Activations
- Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation