Stuck for research ideas? This AI has read 48k papers and wants to help!
A smart research assistant that reads papers and suggests new directions worth exploring
SciPIP, proposed in this paper, combines literature analysis and brainstorming to help researchers generate novel, feasible paper ideas
📚 https://arxiv.org/abs/2410.23166
🎯 Original Problem:
Researchers face challenges in generating novel research ideas due to information overload and complex interdisciplinary requirements. Existing LLM-based idea generators struggle with comprehensive literature retrieval and balancing novelty with feasibility.
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🔧 Solution in this Paper:
→ Built SciPIP: A system combining literature retrieval with dual-path idea generation
→ Created database of 48,895 NLP papers with multi-dimensional info extraction using GLM-4
→ Implemented SEC-based retrieval combining:
- Semantic matching using SentenceBERT embeddings
- Entity-based matching with expanded key terms
- Citation co-occurrence patterns
→ Developed 3 idea generation variants:
- SciPIP-A: Pure literature-inspired
- SciPIP-B: Dual-path with separate brainstorming
- SciPIP-C: Enhanced dual-path using brainstorming for retrieval
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💡 Key Insights:
→ Literature retrieval needs semantic, entity and citation-based approaches for completeness
→ Brainstorming complements literature-based ideation for better novelty
→ Clustering retrieved papers reduces redundancy and noise
→ Entity expansion helps catch papers using different terms for same concepts
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📊 Results:
→ Generated 4-5 ideas matching ACL 2024 papers per 100 backgrounds
→ Achieved 41.9% recall@10 for literature retrieval vs 38.1% baseline
→ Produced 92 highly novel ideas (score 9/10) vs 12 from baseline
→ Maintained feasibility across different novelty levels (19.1-25.5% win rate)
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