Instruction-Tuned Language Model
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An Instruction-Tuned Language Model is a fine-tuned neural language model that can follow instruction-tuned natural language instructions through instruction-tuned training optimization.
- AKA: Instruction-Following Language Model, Task-Instructed Model, Command-Aligned Language Model.
- Context:
- It can typically interpret Instruction-Tuned Natural Language Command through instruction-tuned semantic parsing.
- It can typically execute Instruction-Tuned Task Directive via instruction-tuned action generation.
- It can typically maintain Instruction-Tuned Context Alignment with instruction-tuned attention mechanisms.
- It can typically demonstrate Instruction-Tuned Zero-Shot Capability through instruction-tuned generalization learning.
- It can typically improve Instruction-Tuned Response Quality using instruction-tuned alignment training.
- It can typically handle Instruction-Tuned Multi-Step Task via instruction-tuned sequential reasoning.
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- It can often utilize Instruction-Tuned Training Dataset such as instruction-tuned human feedback data.
- It can often employ Instruction-Tuned Fine-Tuning Method like instruction-tuned reinforcement learning.
- It can often incorporate Instruction-Tuned Safety Constraint through instruction-tuned constitutional training.
- It can often support Instruction-Tuned Cross-Task Transfer via instruction-tuned meta-learning.
- It can often enable Instruction-Tuned Few-Shot Learning with instruction-tuned prompt engineering.
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- It can range from being a Small Instruction-Tuned Language Model to being a Large Instruction-Tuned Language Model, depending on its instruction-tuned parameter count.
- It can range from being a Narrow Instruction-Tuned Language Model to being a Broad Instruction-Tuned Language Model, depending on its instruction-tuned task diversity.
- It can range from being a Weakly Instruction-Tuned Language Model to being a Strongly Instruction-Tuned Language Model, depending on its instruction-tuned alignment degree.
- It can range from being a Single-Language Instruction-Tuned Model to being a Multi-Language Instruction-Tuned Model, depending on its instruction-tuned linguistic scope.
- It can range from being a Text-Only Instruction-Tuned Model to being a Multi-Modal Instruction-Tuned Model, depending on its instruction-tuned input modality.
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- It can integrate with Instruction-Tuned Prompting System for instruction-tuned task specification.
- It can connect to Instruction-Tuned Evaluation Framework for instruction-tuned performance assessment.
- It can utilize Instruction-Tuned Optimization Pipeline for instruction-tuned training efficiency.
- It can implement Instruction-Tuned Safety Filter for instruction-tuned harm prevention.
- It can employ Instruction-Tuned Caching System for instruction-tuned inference acceleration.
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- Example(s):
- Google Instruction-Tuned Models, such as:
- OpenAI Instruction-Tuned Models, such as:
- Meta Instruction-Tuned Models, such as:
- Open-Source Instruction-Tuned Models, such as:
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- Counter-Example(s):
- Base Language Models, which lack instruction-tuned alignment training.
- Completion-Only Models, which predict next tokens without instruction-tuned task understanding.
- Masked Language Models, which focus on token prediction rather than instruction-tuned command following.
- Retrieval Models, which match documents without instruction-tuned generation capability.
- See: Language Model, Fine-Tuned Model, Large Language Model, Neural Language Model, Task-Oriented Training, Reinforcement Learning from Human Feedback, Prompt Engineering, Zero-Shot Learning, Few-Shot Learning, Model Alignment.