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Literature Review: Expert-Augmented LLM Ideation
1. Core Directly-Related Work
1.1 Wisdom of Crowds via Role Assumption
Bringing the Wisdom of the Crowd to an Individual by Having the Individual Assume Different Roles (ACM C&C 2017)
Groups of people tend to generate more diverse ideas than individuals because each group member brings a different perspective. This study showed it's possible to help individuals think more like a group by asking them to approach a problem from different perspectives. In an experiment with 54 crowd workers, participants who assumed different expert roles came up with more creative ideas than those who did not.
Gap for our work: This was human-based role-playing. Our system automates this with LLM-powered expert perspectives.
1.2 PersonaFlow: LLM-Simulated Expert Perspectives
PersonaFlow: Designing LLM-Simulated Expert Perspectives for Enhanced Research Ideation (2024)
PersonaFlow provides multiple perspectives by using LLMs to simulate domain-specific experts. User studies showed it increased the perceived relevance and creativity of ideated research directions and promoted users' critical thinking activities without increasing perceived cognitive load.
Gap for our work: PersonaFlow focuses on research ideation. Our system applies to product/innovation ideation with structured attribute decomposition.
1.3 PopBlends: Conceptual Blending with LLMs
PopBlends: Strategies for Conceptual Blending with Large Language Models (CHI 2023)
PopBlends automatically suggests conceptual blends using both traditional knowledge extraction and LLMs. Studies showed people found twice as many blend suggestions with the system, with half the mental demand.
Gap for our work: We structure blending through expert domain knowledge rather than direct concept pairing.
1.4 BILLY: Persona Vector Merging
BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation (2025)
Proposes fusing persona vectors in activation space to steer LLM output towards multiple perspectives simultaneously, requiring only a single additive operation during inference.
Gap for our work: We use sequential multi-expert generation rather than vector fusion, allowing more explicit control and interpretability.
2. Theoretical Foundations
2.1 Semantic Distance Theory
Core Insight (Mednick, 1962): Creative thinking involves connecting weakly related, remote concepts in semantic memory. The farther one "moves away" from a conventional idea, the more creative the new idea will likely be.
Key Research:
- Semantic distance plays an important role in the creative process
- A more "flexible" semantic memory structure (higher connectivity, shorter distances) facilitates creative idea generation
- Quantitative measures using LSA and semantic networks can objectively examine creative output
- Divergent Semantic Integration (DSI) correlates strongly with human creativity ratings (72% variance explained)
Application to Our Work: Expert perspectives force semantic "jumps" to distant domains that LLMs wouldn't naturally traverse.
Without Expert: "Chair" → furniture, sitting, comfort (short semantic distance)
With Expert: "Chair" + Marine Biologist → pressure, buoyancy, coral (long semantic distance)
2.2 Conceptual Blending Theory
Core Insight (Fauconnier & Turner, 2002): Creative products emerge from blending elements of two input spaces into a novel integrated space.
Key Research:
- Blending process: (1) find connecting concept between inputs, (2) map elements that can be blended
- Generative AI demonstrates ability to blend and integrate concepts (bisociation)
- Trisociation (three-concept blending) is being used for AI-augmented idea generation
- Conceptual blending provides terminology for describing creative products
Limitation: Blending theory doesn't explain where inputs originate - the "inspiration problem."
Application to Our Work: Each expert provides a distinct "input space" enabling systematic multi-space blending. Our attribute decomposition provides structured inputs for blending.
2.3 Design Fixation
Core Insight (Jansson & Smith, 1991): Design fixation is "blind adherence to a set of ideas or concepts limiting the output of conceptual design."
Key Research:
- Fixation results from categorical knowledge organization around prototypes
- Accessing prototypes requires less cognitive effort than processing exemplars
- Diverse teams, model-making, and facilitation help prevent fixation
- Reflecting on prior fixation episodes is most effective prevention
Neural Evidence: fMRI studies show distinct patterns during fixated vs. creative ideation.
Application to Our Work: LLMs exhibit "semantic fixation" on high-probability outputs. Expert perspectives break this by forcing activation of non-prototype knowledge.
2.4 Constraint-Based Creativity
Core Insight: Paradoxically, constraints can enhance creativity by pushing beyond the path of least resistance.
Key Research:
- Constraints push people to search for more distant ideas in semantic memory
- Extreme constraints may require different types of creative problem-solving
- Not all constraints promote creativity for all individuals/tasks
- A "constraint-leveraging mindset" can be developed through experience
Application to Our Work: Expert role = productive constraint that expands rather than limits creative space. The expert perspective forces exploration of non-obvious solution spaces.
3. LLM Limitations in Creative Generation
3.1 Design Fixation from AI
The Effects of Generative AI on Design Fixation and Divergent Thinking (CHI 2024)
Key finding: AI exposure during ideation leads to HIGHER fixation. Participants who used AI produced:
- Fewer ideas
- Less variety
- Lower originality
compared to baseline (no AI assistance).
3.2 Dual Mechanisms: Inspiration vs. Fixation
Inspiration Booster or Creative Fixation? (Nature Humanities & Social Sciences, 2025)
- LLMs help in simple creative tasks (inspiration stimulation)
- LLMs hurt in complex creative tasks (creative fixation)
Application to Our Work: Our structured decomposition manages complexity, while multi-expert approach maintains inspiration benefits.
3.3 Statistical Pattern Perpetuation
Bias and Fairness in Large Language Models: A Survey (MIT Press, 2024)
LLMs learn, perpetuate, and amplify patterns from training data. This applies to creative outputs - LLMs generate what is statistically common/expected.
3.4 Generalization Bias
Generalization Bias in LLM Summarization (Royal Society, 2025)
LLMs' overgeneralization tendency produces outputs that lack sufficient empirical support. This suggests a bias toward "safe" middle-ground outputs rather than novel extremes.
4. Role-Playing and Perspective-Taking
4.1 Creativity Enhancement
Research on tabletop role-playing games (TTRPGs) demonstrates:
- Significant positive impact on creativity potential through divergent thinking
- TTRPG players exhibit significantly higher creativity than non-players
- Perspective-taking is closely linked to empathy and cognitive flexibility
4.2 Therapeutic and Educational Applications
- Role-playing develops perspective-taking, storytelling, creativity, and self-expression
- Physiological, emotional, and mental well-being from play enables creative ideation
- Play signals psychological safety, which is essential for creativity
4.3 Design Research Applications
- Role-playing stimulates creativity by exploring alternative solutions
- Offers safe environment to explore failure modes and challenge assumptions
- Well-suited for early-stage ideation and empathy-critical moments
5. Creativity Support Tools (CSTs)
5.1 Current State
- CSTs primarily support divergent thinking
- Convergent thinking often neglected
- Ideal CST should offer tailored support for both
5.2 AI as Creative Partner
- Collaborative ideation systems expose users to different ideas
- Competing theories on when/whether such exposure helps
- Tool-mediated expert activity view: computers as "mediating artifacts people act through"
5.3 Evaluation Methods
Consensual Assessment Technique (CAT):
- Pool of experts independently evaluate artifacts
- Creative if high evaluations + high interrater reliability (Cronbach's alpha > 0.7)
Semantic Distance Measures:
- SemDis platform for automated creativity assessment
- Overcomes labor cost and subjectivity of human rating
- Uses NLP to quantify semantic relatedness
6. Our Theoretical Contribution
The "Semantic Gravity" Problem
Direct LLM Generation:
P(idea | query)
→ Samples from high-probability region
→ Ideas cluster around training distribution modes
→ "Semantic gravity" pulls toward conventional associations
Expert Transformation Solution
Conditioned Generation:
P(idea | query, expert)
→ Expert perspective activates distant semantic regions
→ Forces conceptual blending across domains
→ Breaks design fixation through productive constraints
Multi-Expert Aggregation
Diverse Experts → Semantic Coverage
→ "Inner crowd" wisdom without actual crowd
→ Systematic exploration of idea space
→ Deduplication ensures non-redundant novelty
Theoretical Model
- Attribute Decomposition: Structures the problem space (categories, attributes)
- Expert Perspectives: Forces semantic jumps to distant domains
- Multi-Expert Aggregation: Achieves crowd-like diversity individually
- Deduplication: Ensures generated ideas are truly distinct
- Patent Validation: Grounds novelty in real-world uniqueness