Harnessing Large Language Models for Scientific Novelty Detection
In an era of exponential scientific growth, identifying novel research ideas
is crucial and challenging in academia. Despite potential, the lack of an
appropriate benchmark dataset hinders the research of novelty detection. More
importantly, simply adopting existing NLP technologies, e.g., retrieving and
then cross-checking, is not a one-size-fits-all solution due to the gap between
textual similarity and idea conception. In this paper, we propose to harness
large language models (LLMs) for scientific novelty detection (ND), associated
with two new datasets in marketing and NLP domains. To construct the
considerate datasets for ND, we propose to extract closure sets of papers based
on their relationship, and then summarize their main ideas based on LLMs. To
capture idea conception, we propose to train a lightweight retriever by
distilling the idea-level knowledge from LLMs to align ideas with similar
conception, enabling efficient and accurate idea retrieval for LLM novelty
detection. Experiments show our method consistently outperforms others on the
proposed benchmark datasets for idea retrieval and ND tasks. Codes and data are
available at https://anonymous.4open.science/r/NoveltyDetection-10FB/.
683d4295c31058e5bf2e2b0b
Yan Liu
683d4295c31058e5bf2e2b0c
Zonglin Yang
claimed_verified
2025-06-02T07:40:15.714Z
646a11791556443f24b582e9
/avatars/b54d416ddca2f124492ba51bc4b95fd2.svg
Zonglin Yang
user
ZonglinY
683d4295c31058e5bf2e2b0d
Soujanya Poria
683d4295c31058e5bf2e2b0e
Thanh-Son Nguyen
683d4295c31058e5bf2e2b0f
Erik Cambria
Harnessing Large Language Models for Scientific Novelty Detection
In an era of exponential scientific growth, identifying novel research ideas
is crucial and challenging in academia. Despite potential, the lack of an
appropriate benchmark dataset hinders the research of novelty detection. More
importantly, simply adopting existing NLP technologies, e.g., retrieving and
then cross-checking, is not a one-size-fits-all solution due to the gap between
textual similarity and idea conception. In this paper, we propose to harness
large language models (LLMs) for scientific novelty detection (ND), associated
with two new datasets in marketing and NLP domains. To construct the
considerate datasets for ND, we propose to extract closure sets of papers based
on their relationship, and then summarize their main ideas based on LLMs. To
capture idea conception, we propose to train a lightweight retriever by
distilling the idea-level knowledge from LLMs to align ideas with similar
conception, enabling efficient and accurate idea retrieval for LLM novelty
detection. Experiments show our method consistently outperforms others on the
proposed benchmark datasets for idea retrieval and ND tasks. Codes and data are
available at https://anonymous.4open.science/r/NoveltyDetection-10FB/.
A method utilizing large language models to detect scientific novelty by distilling idea-level knowledge and constructing specialized datasets in marketing and NLP domains.
large language models scientific novelty detection closure sets idea retrieval idea conception lightweight retriever knowledge distillation