IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback
- URL: http://arxiv.org/abs/2410.04025v1
- Date: Sat, 5 Oct 2024 04:06:07 GMT
- Title: IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback
- Authors: Kevin Pu, K. J. Kevin Feng, Tovi Grossman, Tom Hope, Bhavana Dalvi Mishra, Matt Latzke, Jonathan Bragg, Joseph Chee Chang, Pao Siangliulue,
- Abstract summary: Idea Synth is a research idea development system that uses literature-grounded feedback for articulating research problems, solutions, evaluations and contributions.
Our lab study (N) showed that participants, while using Idea Synth, explored more alternative ideas and expanded initial ideas with more details compared to a strong LLM-based baseline.
Our deployment study (N=7) demonstrated that participants effectively used Idea Synth for real-world research projects at various stages from developing initial ideas to revising framings of mature manuscripts.
- Score: 26.860080743555283
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Research ideation involves broad exploring and deep refining ideas. Both require deep engagement with literature. Existing tools focus primarily on idea broad generation, yet offer little support for iterative specification, refinement, and evaluation needed to further develop initial ideas. To bridge this gap, we introduce IdeaSynth, a research idea development system that uses LLMs to provide literature-grounded feedback for articulating research problems, solutions, evaluations, and contributions. IdeaSynth represents these idea facets as nodes on a canvas, and allow researchers to iteratively refine them by creating and exploring variations and composing them. Our lab study (N=20) showed that participants, while using IdeaSynth, explored more alternative ideas and expanded initial ideas with more details compared to a strong LLM-based baseline. Our deployment study (N=7) demonstrated that participants effectively used IdeaSynth for real-world research projects at various ideation stages from developing initial ideas to revising framings of mature manuscripts, highlighting the possibilities to adopt IdeaSynth in researcher's workflows.
Related papers
- SciPIP: An LLM-based Scientific Paper Idea Proposer [30.670219064905677]
We introduce SciPIP, an innovative framework designed to enhance the proposal of scientific ideas through improvements in both literature retrieval and idea generation.
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.
arXiv Detail & Related papers (2024-10-30T16:18:22Z) - Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents [64.64280477958283]
An exponential increase in scientific literature makes it challenging for researchers to stay current with recent advances and identify meaningful research directions.
Recent developments in large language models(LLMs) suggest a promising avenue for automating the generation of novel research ideas.
We propose a Chain-of-Ideas(CoI) agent, an LLM-based agent that organizes relevant literature in a chain structure to effectively mirror the progressive development in a research domain.
arXiv Detail & Related papers (2024-10-17T03:26:37Z) - Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System [62.832818186789545]
Virtual Scientists (VirSci) is a multi-agent system designed to mimic the teamwork inherent in scientific research.
VirSci organizes a team of agents to collaboratively generate, evaluate, and refine research ideas.
We show that this multi-agent approach outperforms the state-of-the-art method in producing novel scientific ideas.
arXiv Detail & Related papers (2024-10-12T07:16:22Z) - Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination [23.48126633604684]
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.
Scideator also helps users to gauge idea novelty by searching the literature for potential overlaps.
arXiv Detail & Related papers (2024-09-23T00:09:34Z) - Good Idea or Not, Representation of LLM Could Tell [86.36317971482755]
We focus on idea assessment, which aims to leverage the knowledge of large language models to assess the merit of scientific ideas.
We release a benchmark dataset from nearly four thousand manuscript papers with full texts, meticulously designed to train and evaluate the performance of different approaches to this task.
Our findings suggest that the representations of large language models hold more potential in quantifying the value of ideas than their generative outputs.
arXiv Detail & Related papers (2024-09-07T02:07:22Z) - Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers [90.26363107905344]
Large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery.
No evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas.
arXiv Detail & Related papers (2024-09-06T08:25:03Z) - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
ResearchAgent is an AI-based system for ideation and operationalization of novel work.
ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.
We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
arXiv Detail & Related papers (2024-04-11T13:36:29Z) - Exploring and Verbalizing Academic Ideas by Concept Co-occurrence [42.16213986603552]
This study devises a framework based on concept co-occurrence for academic idea inspiration.
We construct evolving concept graphs according to the co-occurrence relationship of concepts from 20 disciplines or topics.
We generate a description of an idea based on a new data structure called co-occurrence citation quintuple.
arXiv Detail & Related papers (2023-06-04T07:01:30Z) - SciMON: Scientific Inspiration Machines Optimized for Novelty [68.46036589035539]
We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature.
We take a dramatic departure with a novel setting in which models use as input background contexts.
We present SciMON, a modeling framework that uses retrieval of "inspirations" from past scientific papers.
arXiv Detail & Related papers (2023-05-23T17:12:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.