# Paper Outline: Expert-Augmented LLM Ideation

## Suggested Titles

1. **"Breaking Semantic Gravity: Expert-Augmented LLM Ideation for Enhanced Creativity"**
2. "Beyond Interpolation: Multi-Expert Perspectives for Combinatorial Innovation"
3. "Escaping the Relevance Trap: Structured Expert Frameworks for Creative AI"
4. "From Crowd to Expert: Simulating Diverse Perspectives for LLM-Based Ideation"

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## Abstract (Draft)

Large Language Models (LLMs) are increasingly used for creative ideation, yet they exhibit a phenomenon we term "semantic gravity" - the tendency to generate outputs clustered around high-probability regions of their training distribution. This limits the novelty and diversity of generated ideas. We investigate two complementary strategies to overcome this limitation: (1) **attribute decomposition**, which structures the problem space before creative exploration, and (2) **expert perspective transformation**, which conditions LLM generation on simulated domain-expert viewpoints. Through a 2×2 factorial experiment comparing Direct generation, Expert-Only, Attribute-Only, and Full Pipeline (both factors combined), we demonstrate that each factor independently improves semantic diversity, with the combination producing super-additive effects. Our Full Pipeline achieves [X]% higher semantic diversity and [Y]% lower patent overlap compared to direct generation. We contribute a theoretical framework explaining LLM creativity limitations and an open-source system for innovation ideation.

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## 1. Introduction

### 1.1 The Promise and Problem of LLM Creativity
- LLMs widely adopted for creative tasks
- Initial enthusiasm: infinite idea generation
- Emerging concern: quality and diversity issues

### 1.2 The Semantic Gravity Problem
- Define the phenomenon
- Why it occurs (statistical learning, mode collapse)
- Why it matters (innovation requires novelty)

### 1.3 Our Solution: Expert-Augmented Ideation
- Brief overview of the approach
- Key insight: expert perspectives as semantic "escape velocity"
- Contributions preview

### 1.4 Paper Organization
- Roadmap for the rest of the paper

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## 2. Related Work

### 2.1 Theoretical Foundations
- Semantic distance and creativity (Mednick, 1962)
- Conceptual blending theory (Fauconnier & Turner)
- Design fixation (Jansson & Smith)
- Constraint-based creativity

### 2.2 LLM Limitations in Creative Generation
- Design fixation from AI (CHI 2024)
- Dual mechanisms: inspiration vs. fixation
- Bias and pattern perpetuation

### 2.3 Persona-Based Prompting
- PersonaFlow (2024)
- BILLY persona vectors (2025)
- Quantifying persona effects (ACL 2024)

### 2.4 Creativity Support Tools
- Wisdom of crowds approaches
- Human-AI collaboration in ideation
- Evaluation methods (CAT, semantic distance)

### 2.5 Positioning Our Work

**Key distinction from PersonaFlow (closest related work)**:
```
PersonaFlow:   Query → Experts → Ideas (no problem structure)
Our approach:  Query → Attributes → (Attributes × Experts) → Ideas
```

- PersonaFlow applies experts to whole query; we apply experts to decomposed attributes
- PersonaFlow cannot isolate what helps; our 2×2 factorial design tests each factor
- We hypothesize attribute decomposition **amplifies** expert effectiveness (interaction effect)
- PersonaFlow showed experts help; we test whether **structuring the problem first** makes experts more effective

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## 3. System Design

### 3.1 Overview
- Pipeline diagram
- Design rationale

### 3.2 Attribute Decomposition
- Category analysis (dynamic vs. fixed)
- Attribute generation per category
- DAG relationship mapping

### 3.3 Expert Team Generation
- Expert sources: LLM-generated, curated, external databases
- Diversity optimization strategies
- Domain coverage considerations

### 3.4 Expert Transformation
- Conditioning mechanism
- Keyword generation
- Description generation
- Parallel processing for efficiency

### 3.5 Semantic Deduplication
- Embedding-based approach
- LLM-based approach
- Threshold selection

### 3.6 Novelty Validation
- Patent search integration
- Overlap scoring

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## 4. Experiments

### 4.1 Research Questions
- RQ1: Does attribute decomposition improve semantic diversity?
- RQ2: Does expert perspective transformation improve semantic diversity?
- RQ3: Is there an interaction effect between the two factors?
- RQ4: Which combination produces the highest patent novelty?
- RQ5: How do expert sources (LLM vs Curated vs External) affect quality?
- RQ6: What is the hallucination/nonsense rate of context-free keyword generation?

### 4.1.1 Design Note: Context-Free Keyword Generation
Our system intentionally excludes the original query during keyword generation:
- Stage 1: Expert sees attribute only (e.g., "wood" + "accountant"), NOT the query ("chair")
- Stage 2: Expert applies keyword to original query with context
- Rationale: Maximize semantic distance for novelty
- Risk: Some ideas may be too distant (nonsense/hallucination)
- RQ6 investigates this tradeoff

### 4.2 Experimental Setup

#### 4.2.1 Dataset
- 30 queries for ideation (see experimental_protocol.md)
- Selection criteria: diverse domains, complexity levels
- Categories: everyday objects, technology/tools, services/systems

#### 4.2.2 Conditions (2×2 Factorial Design)
| Condition | Attributes | Experts | Description |
|-----------|------------|---------|-------------|
| **C1: Direct** | ❌ | ❌ | Baseline: "Generate 20 creative ideas for [query]" |
| **C2: Expert-Only** | ❌ | ✅ | Expert personas generate for whole query |
| **C3: Attribute-Only** | ✅ | ❌ | Decompose query, direct generate per attribute |
| **C4: Full Pipeline** | ✅ | ✅ | Decompose query, experts generate per attribute |
| **C5: Random-Perspective** | ❌ | (random) | Control: 4 random words as "perspectives" |

#### 4.2.3 Controls
- Same LLM model (specify version)
- Same temperature settings
- Same total idea count per condition (20 ideas)

### 4.3 Metrics

#### 4.3.1 Semantic Diversity
- Mean pairwise cosine distance between embeddings
- Cluster distribution analysis
- Silhouette score for idea clustering

#### 4.3.2 Novelty
- Patent overlap rate
- Semantic distance from query centroid

#### 4.3.3 Quality (Human Evaluation)
- Novelty rating (1-7 Likert)
- Usefulness rating (1-7 Likert)
- Creativity rating (1-7 Likert)
- **Relevance rating (1-7 Likert) - for RQ6**
- Interrater reliability (Cronbach's alpha)

#### 4.3.4 Nonsense/Hallucination Analysis (RQ6) - Three Methods
| Method | Metric | Purpose |
|--------|--------|---------|
| Automatic | Semantic distance threshold (>0.85) | Fast screening |
| LLM-as-Judge | GPT-4 relevance score (1-3) | Scalable evaluation |
| Human | Relevance rating (1-7 Likert) | Gold standard validation |

Triangulate all three to validate findings

### 4.4 Procedure
- Idea generation process
- Evaluation process
- Statistical analysis methods

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## 5. Results

### 5.1 Main Effect of Attribute Decomposition (RQ1)
- Compare: (Attribute-Only + Full Pipeline) vs (Direct + Expert-Only)
- Quantitative results
- Statistical significance (ANOVA main effect)

### 5.2 Main Effect of Expert Perspectives (RQ2)
- Compare: (Expert-Only + Full Pipeline) vs (Direct + Attribute-Only)
- Quantitative results
- Statistical significance (ANOVA main effect)

### 5.3 Interaction Effect (RQ3)
- 2×2 interaction analysis
- Visualization: interaction plot
- Evidence for super-additive vs additive effects

### 5.4 Patent Novelty (RQ4)
- Overlap rates by condition
- Full Pipeline vs other conditions
- Examples of high-novelty ideas

### 5.5 Expert Source Comparison (RQ5)
- LLM-generated vs curated vs external
- Unconventionality metrics
- Within Expert=With conditions only

### 5.6 Control Condition Analysis
- Expert-Only vs Random-Perspective
- Validates expert knowledge matters

### 5.7 Hallucination/Nonsense Analysis (RQ6)
- Nonsense rate by condition (LLM-as-judge)
- Semantic distance threshold analysis
- Novelty-usefulness tradeoff visualization
- Is the context-free design worth the hallucination cost?

### 5.8 Human Evaluation Results
- Rating distributions by condition
- 2×2 pattern in human judgments
- Correlation with automatic metrics

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## 6. Discussion

### 6.1 Interpreting the Results
- Why each factor contributes independently
- The interaction: why attributes amplify expert effectiveness
- Theoretical explanation via conceptual blending

### 6.2 Theoretical Implications
- Semantic gravity as framework for LLM creativity
- Two complementary escape mechanisms
- Structured decomposition as "scaffolding" for creative exploration

### 6.3 Practical Implications
- When to use multi-expert approach
- Expert selection strategies
- Integration with existing workflows

### 6.4 Limitations
- LLM-specific results may not generalize
- Patent overlap as proxy for true novelty
- Human evaluation subjectivity
- Single-language experiments

### 6.5 Future Work
- Cross-cultural creativity
- Domain-specific expert optimization
- Real-world deployment studies
- Integration with other creativity techniques

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## 7. Conclusion

- Summary of contributions
- Key takeaways
- Broader impact

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## Appendices

### A. Prompt Templates
- Expert generation prompts
- Keyword generation prompts
- Description generation prompts

### B. Full Experimental Results
- Complete data tables
- Additional visualizations

### C. Expert Source Details
- Curated occupation list
- DBpedia/Wikidata query details

### D. Human Evaluation Protocol
- Instructions for raters
- Example ratings
- Training materials

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## Target Venues

### Tier 1 (Recommended)
1. **CHI** - ACM Conference on Human Factors in Computing Systems
   - Strong fit: creativity support tools, human-AI collaboration
   - Deadline: typically September

2. **CSCW** - ACM Conference on Computer-Supported Cooperative Work
   - Good fit: collaborative ideation, crowd wisdom
   - Deadline: typically April/January

3. **Creativity & Cognition** - ACM Conference
   - Perfect fit: computational creativity focus
   - Smaller but specialized venue

### Tier 2 (Alternative)
4. **DIS** - ACM Designing Interactive Systems
   - Good fit: design ideation tools

5. **UIST** - ACM Symposium on User Interface Software and Technology
   - If system/interaction focus emphasized

6. **ICCC** - International Conference on Computational Creativity
   - Specialized computational creativity venue

### Journal Options
1. **International Journal of Human-Computer Studies (IJHCS)**
2. **ACM Transactions on Computer-Human Interaction (TOCHI)**
3. **Design Studies**
4. **Creativity Research Journal**

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## Timeline Checklist

- [ ] Finalize experimental design
- [ ] Collect/select query dataset
- [ ] Run all experimental conditions
- [ ] Compute automatic metrics
- [ ] Design human evaluation study
- [ ] Recruit evaluators
- [ ] Conduct human evaluation
- [ ] Statistical analysis
- [ ] Write first draft
- [ ] Internal review
- [ ] Revision
- [ ] Submit