Why choose this project topic?
A study of reducing inference cost while preserving a bounded intent classifier's behaviour gives this topic a concrete purpose beyond simply adding an AI model. Comparing intent F1, inference latency and teacher-student disagreement helps a student demonstrate capability, inspect failure cases and explain where human review or a simpler method remains necessary.
How effectively can small-model distillation for intent tasks support reducing inference cost while preserving a bounded intent classifier's behaviour, evaluated using intent F1, inference latency and teacher-student disagreement?
Choose a feasible licensed or synthetic dataset and available compute budget, then agree a narrow evaluation for reducing inference cost while preserving a bounded intent classifier's behaviour with your supervisor.
Proposed project objectives
- 01Define the task, evidence and evaluation assumptions for reducing inference cost while preserving a bounded intent classifier's behaviour.
- 02Use a licensed intent corpus, train a compact student from permitted teacher outputs and compare accuracy and calibration on untouched examples.
- 03Measure intent F1, inference latency and teacher-student disagreement against a stated baseline and analyse failure cases.
A suggested research approach
Use a licensed intent corpus, train a compact student from permitted teacher outputs and compare accuracy and calibration on untouched examples. Check dataset permissions and keep evaluation examples separate from model development. Record model versions, prompts or training settings, then compare intent F1, inference latency and teacher-student disagreement using repeatable runs and retain incorrect outputs for a transparent limitations discussion.
What you will need
- Licensed intent data
- Permitted teacher outputs
- Compact model training tools
Keep your project scope clear
Distillation may reproduce teacher errors and does not establish equivalent behaviour outside the test task. Generated project writing does not include a trained or deployed AI application.
Artificial Intelligence project chapter outline
Use this outline as a starting point. You can edit the chapter titles to match your department’s format during setup.
- Chapter 1Introduction
- Chapter 2Literature Review
- Chapter 3System Analysis and Design
- Chapter 4System Implementation and Testing
- Chapter 5Summary, Conclusion and Recommendations
Turn this topic into your own final year project.
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