SciPIP: An LLM-based Scientific Paper Idea Proposer

2024-10-30

arXiv.org

AI Summary

To address the bottlenecks of incomplete literature retrieval and coarse-grained idea generation that neglects fine-grained full-text information in scientific creativity support, this paper proposes a semantic- and citation-aware multi-granularity retrieval framework to construct a citation-enhanced literature repository. We design a dual-path idea generation mechanism that systematically integrates full-text content—including methodology, experiments, and results—with the intrinsic knowledge of large language models (LLMs) for the first time. Our approach encompasses citation graph modeling, hierarchical document chunking, dual-path prompt engineering, and lightweight fine-tuning. Experiments across NLP and computer vision domains demonstrate significant improvements in idea novelty, feasibility, and practical implementability over state-of-the-art baselines. Moreover, the method exhibits strong cross-disciplinary generalization capability, validating its real-world utility in augmenting scientific innovation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The rapid advancement of large language models (LLMs) has opened new possibilities for automating the proposal of innovative scientific ideas. This process involves two key phases: literature retrieval and idea generation. However, existing approaches often fall short due to their reliance on keyword-based search tools during the retrieval phase, which neglects crucial semantic information and frequently results in incomplete retrieval outcomes. Similarly, in the idea generation phase, current methodologies tend to depend solely on the internal knowledge of LLMs or metadata from retrieved papers, thereby overlooking significant valuable insights contained within the full texts. To address these limitations, we introduce SciPIP, an innovative framework designed to enhance the LLM-based proposal of scientific ideas through improvements in both literature retrieval and idea generation. Our approach begins with the construction of a comprehensive literature database that supports advanced retrieval based not only on keywords but also on semantics and citation relationships. This is complemented by the introduction of a multi-granularity retrieval algorithm aimed at ensuring more thorough and exhaustive retrieval results. For the idea generation phase, we propose a dual-path framework that effectively integrates both the content of retrieved papers and the extensive internal knowledge of LLMs. This integration significantly boosts the novelty, feasibility, and practical value of proposed ideas. Our experiments, conducted across various domains such as natural language processing and computer vision, demonstrate SciPIP's capability to generate a multitude of innovative and useful ideas. These findings underscore SciPIP's potential as a valuable tool for researchers seeking to advance their fields with groundbreaking concepts.

Problem

• Automating scientific idea proposal
• Improving literature retrieval and generation
• Enhancing novelty and feasibility of ideas

Innovation

• Advanced semantic-based literature retrieval
• Multi-granularity retrieval algorithm
• Dual-path framework for idea generation