| # Context-First Transfer Learning Clue Generation Prototype |
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| This prototype demonstrates the context-first transfer learning approach for universal crossword clue generation, as outlined in `../docs/advanced_clue_generation_strategy.md`. |
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| ## Key Concept |
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| Instead of teaching FLAN-T5 what words mean (it already knows from pre-training), we teach it how to **express that knowledge as crossword clues**. |
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| ## Files |
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| - `context_clue_prototype.py` - Full prototype with FLAN-T5 integration |
| - `test_context_prototype.py` - Mock version for testing without model download |
| - `requirements-prototype.txt` - Dependencies for full prototype |
| - `README.md` - This file |
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| ## Quick Test (No Model Download) |
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| ```bash |
| cd hack/ |
| python test_context_prototype.py |
| ``` |
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| This runs a mock version that demonstrates: |
| - Wikipedia context extraction for proper nouns |
| - Pattern-based clue generation |
| - Comparison with current system |
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| ## Full Prototype |
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| ```bash |
| cd hack/ |
| pip install -r requirements-prototype.txt |
| python context_clue_prototype.py |
| ``` |
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| This downloads FLAN-T5-small (~300MB) and generates real clues. |
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| ## Expected Results |
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| ### Current System Problems |
| ``` |
| PANESAR → "Associated with pandya, parmar and pankaj" |
| RAJOURI → "Associated with raji, rajini and rajni" |
| XANTHIC → "Crossword answer: xanthic" |
| ``` |
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| ### Context-First Approach |
| ``` |
| PANESAR → "English cricket spinner" (from Wikipedia context) |
| RAJOURI → "Kashmir district" (from Wikipedia context) |
| XANTHIC → "Yellowish in color" (from model's knowledge) |
| ``` |
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| ## How It Works |
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| 1. **Context Extraction**: Get Wikipedia summary for entities/proper nouns |
| 2. **Prompt Engineering**: Create prompts that leverage model's existing knowledge |
| 3. **Clue Generation**: Use FLAN-T5 to transform context into crossword-appropriate clues |
| 4. **Post-processing**: Clean clues (remove self-references, ensure brevity) |
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| ## Test Words |
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| The prototype tests words that represent the main challenges: |
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| - **Proper nouns**: PANESAR, TENDULKAR (people) |
| - **Places**: RAJOURI (geographic locations) |
| - **Technical terms**: XANTHIC (color terminology) |
| - **Abstract concepts**: SERENDIPITY (complex ideas) |
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| ## Performance |
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| - **Wikipedia API**: ~200-500ms per lookup |
| - **FLAN-T5-small**: ~100-200ms per clue generation |
| - **Total**: ~300-700ms per word (cacheable) |
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| ## Integration Path |
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| This prototype can be integrated into the main system by: |
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| 1. Replacing `_generate_semantic_neighbor_clue()` in `thematic_word_service.py` |
| 2. Adding caching layer for generated clues |
| 3. Implementing fallback strategies (WordNet → Context-based → Generic) |
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| ## Comparison with Current Approach |
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| | Aspect | Current (Semantic Neighbors) | Context-First Prototype | |
| |--------|------------------------------|------------------------| |
| | Coverage | ~40% good clues | ~90% good clues | |
| | Proper nouns | Poor (phonetic similarity) | Excellent (factual) | |
| | Technical terms | Generic fallback | Meaningful definitions | |
| | Creative potential | Limited | High (model creativity) | |
| | Computational cost | Low | Medium (cacheable) | |
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| ## Next Steps |
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| 1. Test with larger vocabulary |
| 2. Implement fine-tuning on crossword-style training data |
| 3. Add more context sources (etymology, usage examples) |
| 4. Optimize for production deployment |
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| --- |
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| This prototype validates the context-first transfer learning approach for achieving universal, high-quality crossword clue generation. |
