REVIEW 5 cited by
Interesting Scientific Idea Generation using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
The rapid growth of scientific literature makes it challenging for researchers to identify novel and impactful ideas, especially across disciplines. Modern artificial intelligence (AI) systems offer new approaches, potentially inspiring ideas not conceived by humans alone. But how compelling are these AI-generated ideas, and how can we improve their quality? Here, we introduce SciMuse, which uses 58 million research papers and a large-language model to generate research ideas. We conduct a large-scale evaluation in which over 100 research group leaders -- from natural sciences to humanities -- ranked more than 4,400 personalized ideas based on their interest. This data allows us to predict research interest using (1) supervised neural networks trained on human evaluations, and (2) unsupervised zero-shot ranking with large-language models. Our results demonstrate how future systems can help generating compelling research ideas and foster unforeseen interdisciplinary collaborations.
Forward citations
Cited by 5 Pith papers
-
ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes
Conference accept/reject outcomes yield 15 operational ideation patterns that, as an LLM skill suite, improve automated-judged research-proposal quality over no-skill and generic-skill baselines.
-
How do Humans and Language Models Reason About Creativity? A Comparative Analysis
When LLMs rate STEM solution originality, showing example solutions improves accuracy but sharply increases correlations among creativity facets to near 1, unlike human raters.
-
AI4Research: A Survey of Artificial Intelligence for Scientific Research
A survey that organizes AI-for-research work into five tasks, comprehension, survey, discovery, writing, and peer review, and compiles associated tools and benchmarks.
-
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research
The authors propose and demonstrate a human-AI teaming toolbox (ChatBCI) for BCI/EEG research, but the claimed speedups and learning gains are supported only by a qualitative case study.
-
Transformational Creativity in Science: A Graphical Theory
Scientific conceptual spaces are formalized as DAGs, and a proof shows that modifying axioms, the sink nodes, has maximal transformative potential.
Discussion (0). Continue with ORCID to comment.