39 lines
1.6 KiB
Markdown
39 lines
1.6 KiB
Markdown
# Research: Expert-Augmented LLM Ideation
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This folder contains research materials for the academic paper on the novelty-seeking system.
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## Files
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| File | Description |
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| `literature_review.md` | Comprehensive literature review covering semantic distance theory, conceptual blending, design fixation, LLM limitations, and related work |
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| `references.md` | 55+ academic references with links to papers |
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| `theoretical_framework.md` | The "Semantic Gravity" theoretical model and testable hypotheses |
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| `paper_outline.md` | Complete paper structure, experimental design, and target venues |
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## Key Theoretical Contribution
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**"Semantic Gravity"**: LLMs exhibit a tendency to generate outputs clustered around high-probability regions of their training distribution, limiting creative novelty. Expert perspectives provide "escape velocity" to break free from this gravity.
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## Core Hypotheses
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1. **H1**: Multi-expert generation → higher semantic diversity
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2. **H2**: Multi-expert generation → lower patent overlap (higher novelty)
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3. **H3**: Diversity increases with expert count (diminishing returns ~4-6)
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4. **H4**: Expert source affects unconventionality of ideas
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## Target Venues
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- **CHI** (ACM Conference on Human Factors in Computing Systems)
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- **CSCW** (ACM Conference on Computer-Supported Cooperative Work)
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- **Creativity & Cognition** (ACM Conference)
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- **IJHCS** (International Journal of Human-Computer Studies)
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## Next Steps
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1. Design concrete experiment protocol
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2. Add measurement code to existing system
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3. Collect experimental data
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4. Conduct human evaluation
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5. Write and submit paper
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