title: Harnessing Large Language Models for Scientific Novelty Detection
summary: 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/.
paper_authors._id: 683d4295c31058e5bf2e2b0b
paper_authors.name: Yan Liu
paper_authors._id: 683d4295c31058e5bf2e2b0c
paper_authors.name: Zonglin Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:40:15.714Z
paper_authors._id: 646a11791556443f24b582e9
paper_authors.avatarUrl: /avatars/b54d416ddca2f124492ba51bc4b95fd2.svg
paper_authors.fullname: Zonglin Yang
paper_authors.type: user
paper_authors.user: ZonglinY
paper_authors._id: 683d4295c31058e5bf2e2b0d
paper_authors.name: Soujanya Poria
paper_authors._id: 683d4295c31058e5bf2e2b0e
paper_authors.name: Thanh-Son Nguyen
paper_authors._id: 683d4295c31058e5bf2e2b0f
paper_authors.name: Erik Cambria
paper_title: Harnessing Large Language Models for Scientific Novelty Detection
paper_summary: 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/.
paper_ai_summary: A method utilizing large language models to detect scientific novelty by distilling idea-level knowledge and constructing specialized datasets in marketing and NLP domains.
paper_ai_keywords: large language models scientific novelty detection closure sets idea retrieval idea conception lightweight retriever knowledge distillation