289 lines
8.1 KiB
Markdown
289 lines
8.1 KiB
Markdown
# 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"
|
||
|
||
---
|
||
|
||
## 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 propose a multi-expert transformation framework that systematically activates diverse semantic regions by conditioning LLM generation on simulated expert perspectives. Our system decomposes concepts into structured attributes, generates ideas through multiple domain-expert viewpoints, and employs semantic deduplication to ensure genuine diversity. Through experiments comparing multi-expert generation against direct LLM generation and single-expert baselines, we demonstrate that our approach produces ideas with [X]% higher semantic diversity and [Y]% lower patent overlap. We contribute a theoretical framework explaining LLM creativity limitations and an open-source system for innovation ideation.
|
||
|
||
---
|
||
|
||
## 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
|
||
|
||
---
|
||
|
||
## 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
|
||
- Gap: No end-to-end system combining structured decomposition + multi-expert transformation + deduplication
|
||
- Distinction from PersonaFlow: product innovation focus, attribute structure
|
||
|
||
---
|
||
|
||
## 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
|
||
|
||
---
|
||
|
||
## 4. Experiments
|
||
|
||
### 4.1 Research Questions
|
||
- RQ1: Does multi-expert generation increase semantic diversity?
|
||
- RQ2: Does multi-expert generation reduce patent overlap?
|
||
- RQ3: What is the optimal number of experts?
|
||
- RQ4: How do expert sources affect output quality?
|
||
|
||
### 4.2 Experimental Setup
|
||
|
||
#### 4.2.1 Dataset
|
||
- N concepts/queries for ideation
|
||
- Selection criteria (diverse domains, complexity levels)
|
||
|
||
#### 4.2.2 Conditions
|
||
| Condition | Description |
|
||
|-----------|-------------|
|
||
| Baseline | Direct LLM: "Generate 20 creative ideas for X" |
|
||
| Single-Expert | 1 expert × 20 ideas |
|
||
| Multi-Expert-4 | 4 experts × 5 ideas each |
|
||
| Multi-Expert-8 | 8 experts × 2-3 ideas each |
|
||
| Random-Perspective | 4 random words as "perspectives" |
|
||
|
||
#### 4.2.3 Controls
|
||
- Same LLM model (specify version)
|
||
- Same temperature settings
|
||
- Same total idea count per condition
|
||
|
||
### 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)
|
||
- Interrater reliability (Cronbach's alpha)
|
||
|
||
### 4.4 Procedure
|
||
- Idea generation process
|
||
- Evaluation process
|
||
- Statistical analysis methods
|
||
|
||
---
|
||
|
||
## 5. Results
|
||
|
||
### 5.1 Semantic Diversity (RQ1)
|
||
- Quantitative results
|
||
- Visualization (t-SNE/UMAP of idea embeddings)
|
||
- Statistical significance tests
|
||
|
||
### 5.2 Patent Novelty (RQ2)
|
||
- Overlap rates by condition
|
||
- Examples of high-novelty ideas
|
||
|
||
### 5.3 Expert Count Analysis (RQ3)
|
||
- Diversity vs. expert count curve
|
||
- Diminishing returns analysis
|
||
- Optimal expert count recommendation
|
||
|
||
### 5.4 Expert Source Comparison (RQ4)
|
||
- LLM-generated vs. curated vs. random
|
||
- Unconventionality metrics
|
||
|
||
### 5.5 Human Evaluation Results
|
||
- Rating distributions
|
||
- Condition comparisons
|
||
- Correlation with automatic metrics
|
||
|
||
---
|
||
|
||
## 6. Discussion
|
||
|
||
### 6.1 Interpreting the Results
|
||
- Why multi-expert works
|
||
- The role of structured decomposition
|
||
- Deduplication importance
|
||
|
||
### 6.2 Theoretical Implications
|
||
- Semantic gravity as framework for LLM creativity
|
||
- Expert perspectives as productive constraints
|
||
- Inner crowd wisdom
|
||
|
||
### 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
|
||
|
||
---
|
||
|
||
## 7. Conclusion
|
||
|
||
- Summary of contributions
|
||
- Key takeaways
|
||
- Broader impact
|
||
|
||
---
|
||
|
||
## 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
|
||
|
||
---
|
||
|
||
## 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**
|
||
|
||
---
|
||
|
||
## 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
|