KITE: Kernelized and Information Theoretic Exemplars for In-Context Learning
Learn how to select optimal task-specific examples for in-context learning with large language models using Kernelized and Information Theoretic Exemplars (KITE) to improve performance on data-scarce tasks
- Build a dataset of task-specific examples
- Apply kernelized and information theoretic methods to select optimal exemplars
- Configure the in-context learning model with the selected exemplars
- Test the performance of the model on a given user query
- Refine the exemplar selection process based on the results
NLP engineers and researchers on a team can benefit from KITE to improve the efficiency of their in-context learning models, while data scientists can apply this knowledge to optimize their language model fine-tuning pipelines
💡 Kernelized and information theoretic methods can be used to select the most informative examples for in-context learning, maximizing performance on a given task
💡 KITE: Selecting optimal examples for in-context learning with LLMs to boost performance on data-scarce tasks
Key Takeaways
Learn how to select optimal task-specific examples for in-context learning with large language models using Kernelized and Information Theoretic Exemplars (KITE) to improve performance on data-scarce tasks
DeepCamp AI