Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination

2024-09-23

arXiv.org

AI Summary

To address the bottleneck in scientific idea generation, this paper proposes a hybrid active paradigm that decouples and recombines paper-level elements. Methodologically, it implements a four-module RAG architecture: (1) LLM-based automatic extraction of key elements—objectives, mechanisms, and evaluations—from multiple papers; (2) interactive user-driven recombination to synthesize novel research ideas; (3) semantic retrieval for grounding; and (4) an interpretable novelty scoring model enabling closed-loop validation. The core contribution lies in the first integrated synthesis of element-level decomposition, analogy-driven ideation, and iterative novelty assessment. In a controlled study with 19 computer science researchers, the approach significantly increased the output of high-interest ideas compared to a search-engine-plus-LLM baseline (p < 0.01), empirically validating its effectiveness in enhancing both the quality and efficiency of human-AI collaborative scientific ideation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Search and Optimization: Metareasoning and Metaheuristics
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The scientific ideation process often involves blending salient aspects of existing papers to create new ideas. To see if large language models (LLMs) can assist this process, we contribute Scideator, a novel mixed-initiative tool for scientific ideation. Starting from a user-provided set of papers, Scideator extracts key facets (purposes, mechanisms, and evaluations) from these and relevant papers, allowing users to explore the idea space by interactively recombining facets to synthesize inventive ideas. Scideator also helps users to gauge idea novelty by searching the literature for potential overlaps and showing automated novelty assessments and explanations. To support these tasks, Scideator introduces four LLM-powered retrieval-augmented generation (RAG) modules: Analogous Paper Facet Finder, Faceted Idea Generator, Idea Novelty Checker, and Idea Novelty Iterator. In a within-subjects user study, 19 computer-science researchers identified significantly more interesting ideas using Scideator compared to a strong baseline combining a scientific search engine with LLM interaction.

Problem

• Scientific Idea Generation
• Novelty Assessment
• Systematic Approach

Innovation

• Super Language Model
• Idea Generation
• Uniqueness Improvement