A Hierarchical Framework for Measuring Scientific Paper Innovation via Large Language Models

• URL: http://arxiv.org/abs/2504.14620v1
• Date: Sun, 20 Apr 2025 13:58:20 GMT
• Title: A Hierarchical Framework for Measuring Scientific Paper Innovation via Large Language Models
• Authors: Hongming Tan, Shaoxiong Zhan, Fengwei Jia, Hai-Tao Zheng, Wai Kin Chan,
• Abstract summary: We propose HSPIM, a hierarchical and training-free framework based on large language models (LLMs)<n>We segment the text by section titles and use zero-shot LLM prompting to implement section classification, question-answering (QA) augmentation, and weighted novelty scoring.<n>To further improve performance, we propose a two-layer question structure consisting of common and section-specific questions, and apply a genetic algorithm to optimize the question-prompt combinations.
• Score: 10.230220606754484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Measuring scientific paper innovation is both important and challenging. Existing content-based methods often overlook the full-paper context, fail to capture the full scope of innovation, and lack generalization. We propose HSPIM, a hierarchical and training-free framework based on large language models (LLMs). It introduces a Paper-to-Sections-to-QAs decomposition to assess innovation. We segment the text by section titles and use zero-shot LLM prompting to implement section classification, question-answering (QA) augmentation, and weighted novelty scoring. The generated QA pair focuses on section-level innovation and serves as additional context to improve the LLM scoring. For each chunk, the LLM outputs a novelty score and a confidence score. We use confidence scores as weights to aggregate novelty scores into a paper-level innovation score. To further improve performance, we propose a two-layer question structure consisting of common and section-specific questions, and apply a genetic algorithm to optimize the question-prompt combinations. Comprehensive experiments on scientific conference paper datasets show that HSPIM outperforms baseline methods in effectiveness, generalization, and interpretability.

Related papers

• Can LLMs Generate Tabular Summaries of Science Papers? Rethinking the Evaluation Protocol [83.90769864167301]
  Literature review tables are essential for summarizing and comparing collections of scientific papers. We explore the task of generating tables that best fulfill a user's informational needs given a collection of scientific papers. Our contributions focus on three key challenges encountered in real-world use: (i) User prompts are often under-specified; (ii) Retrieved candidate papers frequently contain irrelevant content; and (iii) Task evaluation should move beyond shallow text similarity techniques.
  arXiv  Detail & Related papers  (2025-04-14T14:52:28Z)
• CATER: Leveraging LLM to Pioneer a Multidimensional, Reference-Independent Paradigm in Translation Quality Evaluation [0.0]
  Comprehensive AI-assisted Translation Edit Ratio (CATER) is a novel framework for evaluating machine translation (MT) quality.<n>Uses large language models (LLMs) via a carefully designed prompt-based protocol.
  arXiv  Detail & Related papers  (2024-12-15T17:45:34Z)
• Self-Calibrated Listwise Reranking with Large Language Models [137.6557607279876]
  Large language models (LLMs) have been employed in reranking tasks through a sequence-to-sequence approach. This reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets. We propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking.
  arXiv  Detail & Related papers  (2024-11-07T10:31:31Z)
• 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)
• Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions [62.12545440385489]
  Large language models (LLMs) have brought substantial advancements in text generation, but their potential for enhancing classification tasks remains underexplored. We propose a framework for thoroughly investigating fine-tuning LLMs for classification, including both generation- and encoding-based approaches. We instantiate this framework in edit intent classification (EIC), a challenging and underexplored classification task.
  arXiv  Detail & Related papers  (2024-10-02T20:48:28Z)
• Taxonomy Tree Generation from Citation Graph [15.188580557890942]
  HiGTL is a novel end-to-end framework guided by human-provided instructions or preferred topics.<n>We develop a novel taxonomy node verbalization strategy that iteratively generates central concepts for each cluster.<n>Experiments demonstrate that HiGTL effectively produces coherent, high-quality concept.
  arXiv  Detail & Related papers  (2024-10-02T13:02:03Z)
• Systematic Task Exploration with LLMs: A Study in Citation Text Generation [63.50597360948099]
  Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks. We propose a three-component research framework that consists of systematic input manipulation, reference data, and output measurement. We use this framework to explore citation text generation -- a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric.
  arXiv  Detail & Related papers  (2024-07-04T16:41:08Z)
• HGOT: Hierarchical Graph of Thoughts for Retrieval-Augmented In-Context Learning in Factuality Evaluation [20.178644251662316]
  We introduce the hierarchical graph of thoughts (HGOT) to enhance the retrieval of pertinent passages during in-context learning. The framework employs the divide-and-conquer strategy to break down complex queries into manageable sub-queries. It refines self-consistency majority voting for answer selection, which incorporates the recently proposed citation recall and precision metrics.
  arXiv  Detail & Related papers  (2024-02-14T18:41:19Z)
• Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution [48.86322922826514]
  This paper defines a new task of Knowledge-aware Language Model Attribution (KaLMA) First, we extend attribution source from unstructured texts to Knowledge Graph (KG), whose rich structures benefit both the attribution performance and working scenarios. Second, we propose a new Conscious Incompetence" setting considering the incomplete knowledge repository. Third, we propose a comprehensive automatic evaluation metric encompassing text quality, citation quality, and text citation alignment.
  arXiv  Detail & Related papers  (2023-10-09T11:45:59Z)
• Generative Judge for Evaluating Alignment [84.09815387884753]
  We propose a generative judge with 13B parameters, Auto-J, designed to address these challenges. Our model is trained on user queries and LLM-generated responses under massive real-world scenarios. Experimentally, Auto-J outperforms a series of strong competitors, including both open-source and closed-source models.
  arXiv  Detail & Related papers  (2023-10-09T07:27:15Z)

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.