hack/transfer_learning_summary.md

True Transfer Learning vs Pattern Matching

The Problem with Previous Attempts

All previous prototypes fell into the hardcoded pattern trap:

# This is NOT transfer learning:
if 'cricketer' in extract.lower():
    return "Cricket player"
elif 'district' in extract.lower():
    return "Administrative region"

True Transfer Learning Approach

The new true_transfer_learning.py does real transfer learning:

✅ What It Does Right:

1. NO hardcoded patterns - no "if cricketer then..." rules
2. Uses model's knowledge - FLAN-T5 learned about Panesar during training
3. Multiple prompting strategies to find what works:
   • "What is PANESAR known for?"
   • "PANESAR is famous for being:"
   • "Define PANESAR in simple terms:"
4. Tries all strategies and picks the best result
5. Larger model (FLAN-T5-base 850MB vs small 77MB)

Key Insight:

The model already knows from pre-training:

• Panesar is a cricketer
• Tendulkar is a famous Indian batsman
• Beethoven is a composer
• Xanthic means yellowish

We just need to ask the right way to extract that knowledge.

Expected Results

If successful, we should see:

• PANESAR → "English cricket bowler" (from model's training knowledge)
• TENDULKAR → "Indian cricket legend" (not hardcoded)
• XANTHIC → "Yellowish color" (model knows the definition)

Why This Matters

This is the difference between AI and rules:

• Rules: IF cricket THEN "player"
• AI: Model actually understands what these words mean

If this works, we've achieved true transfer learning for crossword clue generation.