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

Hongming Tan, Shaoxiong Zhan, Fengwei Jia, Hai-Tao Zheng, Wai Kin Chan

arXiv:2504.14620v2cs.CL

TL;DR

Content-based approaches can miss full-paper context, practical value, and generalization when measuring scientific-paper innovation. HSPIM uses a training-free, hierarchical LLM framework to score papers from section-level QA and confidence-weighted novelty. The authors report an unbiased full-paper estimate under stated unbiasedness assumptions and probability-one convergence for elitist prompt optimization.

  • Problem

    Content-based methods may focus on selected sections, assess novelty without practical value, and be limited in generalization across topics and publication years.

    Source: §1 · p. 1

  • Method

    HSPIM uses a training-free, section-based LLM framework with QA augmentation and confidence-weighted scoring to measure paper-level innovation.

    Source: §1 · p. 2, §8 · p. 22

  • Results

    Given unbiasedness in expectation for section novelty and confidence scores, weighting yields an unbiased full-paper innovation estimate; elitist GA on a finite discrete space converges to a global minimum with probability 1.

    Source: §4.4 · p. 10

  • Takeaways & Limitations

    HSPIM directly targets innovation measurement with a hierarchical design and provides informative textual outputs alongside innovation quantification.

    Source: §8 · p. 22

    HSPIM depends on pretrained LLM knowledge, which may bias evaluations of papers in training data and make evaluations of emerging fields unreliable.

    Source: §7.2 · p. 20

Abstract

from arXiv · show

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 innovation 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. Furthermore, under the fine-grained structure of innovation, we extend HSPIM to an HSPIM$^+$ that generates novelty, contribution, and feasibility scores with respective confidence scores. Comprehensive experiments on scientific conference paper datasets show that HSPIM outperforms baseline methods in effectiveness, generalization, and interpretability. Demo code is available at https://github.com/Jasaxion/HSPIM.

1. Introduction

Existing content-based methods often analyze only selected paper sections, measure novelty without practical value, and generalize poorly. HSPIM addresses these gaps with section-level QA, confidence-weighted scoring, and optimized prompts.

1. Introduction

  • Content-based methods may miss full-paper context, omit value and feasibility, and generalize poorly beyond specific topics and publication years.The paper also distinguishes innovation—which includes practical value—from novelty alone.Source: §1 · p. 1
  • Confidence-weighted novelty scores aggregate section-level judgments into a paper-level innovation score, while QA augmentation supplies innovation-focused context.The framework defines innovation through novelty and practical value, including contribution and feasibility.Source: §1 · p. 2
  • A genetic algorithm optimizes the common and section-specific question prompts, with joint optimization, two-step optimization, and pruning among the strategies explored.Each GA individual contains one common question plus one specific question for each section type.Source: §1 · p. 2
  • The paper evaluates HSPIM on three PeerRead datasets and one NLPeer dataset, using review originality and soundness/correctness as proxies for novelty and practical value.The introduction describes the approach as the first LLM-based framework for scientific innovation measurement, contrasting it with review-generating systems such as SEA-E.Source: §1 · p. 2
  • HSPIM decomposes papers into sections and QA pairs, using zero-shot LLMs to assess scientific innovation at multiple levels.Text chunks are classified into common section types following the IMRaD format.Source: §1 · pp. 1–2

2. Related Work

The related work motivates treating innovation as more than novelty and reviews measurement and prompt-based LLM approaches. HSPIM combines full-paper section analysis with optimized multi-prompt QA.

2.1. Innovation Concept

  • Innovation is framed as novelty plus practical value, with contribution and real-world feasibility as distinct aspects of that value.Prior accounts also connect feasibility to realizability and implementation.Source: §2.1 · p. 3

2.2. Approaches to Measuring Paper Innovation

  • Prior paper-innovation measures largely target novelty, including atypical combinations of existing knowledge, using content-based or metadata-based approaches.Content-based methods may focus on parts such as titles or contribution sentences, while metadata methods use external attributes.Source: §2.2 · p. 3
  • HSPIM uses training-free LLM prompting and section-based QA to address long-text analysis, innovation quantification, and generalization challenges in content-based methods.The paper motivates zero-shot scoring and QA extraction as ways to assess innovation from paper content.Source: §2.2 · p. 3, §2.3 · p. 3

2.3. Prompt-based LLMs

  • Unlike long-prompt optimization, HSPIM jointly updates multiple question prompts using RMSE feedback for QA pairs across different text chunks.Its question design combines a common prompt with section-specific prompts; prior prompt-optimization approaches include EvoPrompt and OPRO [12].Source: §2.3 · pp. 3–4, Figure 1 · p. 4

3. Methodology

The section outlines the order of its methodological discussion: notations and problem definition, HSPIM and its simplified variant, then the two-layer question structure and prompt-optimization strategies.

3. Methodology

  • The methodology section proceeds from notation and problem definition to the HSPIM comparison, question-structure rationale, and three multi-prompt optimization strategies.Table 1 summarizes the paper's key notations.Source: §3 · p. 4

3.1. Notations and Problem Definition

The paper defines innovation-score prediction against peer-review-derived ground truth and seeks to minimize prediction error. HSPIM aggregates section scores, with HSPIM+ extending the score to additional innovation dimensions.

3.1. Notations and Problem Definition

  • The prediction task assigns numeric innovation scores to papers and minimizes error against ground-truth scores.The ground truth is derived from peer reviews.Source: §3.1 · p. 4
  • Each review's innovation score is the mean of originality and soundness/correctness, and paper-level labels average the review scores.The paper uses these review attributes to quantify novelty and practical value.Source: §3.1 · p. 4
  • HSPIM classifies paper chunks by section type and aggregates LLM-generated novelty scores using confidence scores as weights.It adds section-focused QA context, formed from a shared question and a section-specific question, before scoring.Source: §3.1 · pp. 4–5, Figure 2 · p. 5
  • HSPIM+ combines novelty, contribution, and feasibility through a p-norm, whose value can produce balanced, cumulative, or dominant effects.Source: §3.1 · p. 5

3.1 by modeling innovation as a composite of three attributes: novelty, contribution, and feasibility. For each

HSPIM+ scores novelty, contribution, and feasibility with confidence-weighted attribute means, then aggregates the three scores into an overall innovation score.

3.1 by modeling innovation as a composite of three attributes: novelty, contribution, and feasibility. For each

  • HSPIM+ computes weighted means for novelty, contribution, and feasibility using their corresponding confidence scores as weights.The framework represents the three attribute scores as a vector and maps its chosen p-norm back to the range.Source: §3.1 · p. 5
  • The overall innovation score aggregates the novelty, contribution, and feasibility vector using an L1, L2, or L∞ norm.The selected norm is linearly mapped to the range; the options correspond to mean, Euclidean, and maximum aggregation.Source: §3.1 · p. 5

3.2. HSPIM Framework and Workflow

HSPIM builds a paper-level innovation measure by classifying paper sections, augmenting them with innovation-focused question-answer pairs, and scoring them with LLMs; prompt optimization extends this workflow.

3.2.1. Step-1: Section Classification

  • Zero-shot LLM prompting assigns paper text chunks to nine section types adapted from IMRaD, including separate categories for theoretical and experimental analysis.Chunks are extracted from PDFs by identifying section titles.Source: §3.2.1 · pp. 5–6

3.2.2. Step-2: Question-Answering Augmentation

  • Each section receives a common question and a section-specific question, with the specific prompt targeting its section and the common prompt addressing innovation across sections.The combined question guides LLM-generated answers that are added as context for subsequent scoring.Source: §3.2.2 · p. 6

3.2.3. Step-3: Innovation Scoring

  • LLMs score each section’s text and QA pair for novelty, provide a rationale and confidence score, and use confidence to weight section scores in the paper-level average.The confidence score also indicates the reliability of the section novelty score.Source: §3.2.3 · p. 6

3.2.4. Step-4: Evaluate Predicted Innovation

  • Peer-review innovation targets average originality and soundness/correctness scores across reviews, and RMSE measures prediction error.The method treats soundness/correctness as a proxy for practical value alongside originality as a proxy for novelty.Source: §3.2.4 · p. 6

3.2.5. Step-5: Optimize Question Prompt

  • Prompt optimization uses a genetic algorithm to evaluate and rank question combinations by validation-set RMSE, then selects, crosses over, and mutates prompts across iterations.The search evaluates multiple chunk-prompt combinations, with question generation and scoring able to run in parallel.Source: §3.2.5 · pp. 7–8

3.3. Framework Comparison

The comparison distinguishes three HSPIM variants by their included steps and prompting workflows.

3.3. Framework Comparison

  • SSPIM uses Steps 1, 3, and 4 with single-turn prompting; naive HSPIM adds QA augmentation in a two-turn workflow, while prompt-optimized HSPIM includes all five steps and iterative multi-turn prompt optimization.The novelty and confidence scoring criteria follow ACL-2018 reviewer guidelines, and the QA prompt is adapted from QAG in QAEA-DR.Source: §3.3 · p. 8

3.4. Multi-prompt Optimization Strategies in HSPIM

HSPIM uses a common question and section-specific questions, which can be optimized jointly, in two stages, or with pruning.

3.4. Multi-prompt Optimization Strategies in HSPIM

  • Specific prompts target individual section types, while a common prompt governs innovation attributes across sections [37].The paper analogizes specific prompts to structural genes and common prompts to regulatory genes.Source: §3.4 · p. 8
  • Joint optimization updates the common and all section-specific prompts together in each genetic-algorithm iteration.It is the default strategy and updates all s + 1 prompts.Source: §3.4 · p. 8
  • Two-step optimization first optimizes the common question, then updates the section-specific questions.The method refers to this variant as HSPIMtwo-step.Source: §3.4 · p. 8
  • Pruning selects s′ of the s section types for the weighted novelty calculation, choosing the best-performing subset on training data for test use.The approach is intended to identify important sections and reduce real-world application cost.Source: §3.4 · p. 8

4. Theoretical Analysis of HSPIM

The analysis states assumptions for weighted scoring and prompt optimization, then gives theoretical results for HSPIM and HSPIM+. It concludes that the guarantees depend on those assumptions, including an independence idealization for HSPIM+.

4.2. Convergence of the Weighted Scoring Mechanism

  • Under assumptions A1–A3, the confidence-weighted average of section-level novelty scores is unbiased for paper-level innovation in expectation.The analysis assumes bounded scores, section-based unbiasedness and independence of novelty and confidence, with negligible dependence across chunks.Source: §4.2 · p. 9, §4.1 · p. 9

4.3. Convergence of GA-Based Prompt Optimization

  • With nonzero mutation, an elitist genetic algorithm on the finite prompt-combination space contains the optimum with probability approaching 1 as generations increase.In practice, the authors stop when performance saturates.Source: §4.3 · pp. 9–10

4.4. Overall Effectiveness and Convergence Discussion

  • The analysis links local novelty to global innovation through confidence weighting and states that the elitist GA converges to a global minimum with probability 1.LLM evaluation costs can prompt early stopping, although partial runs can still reduce RMSE relative to single-turn QA.Source: §4.4 · p. 10

4.5. Theoretical Justification of HSPIM+

  • HSPIM+ models composite innovation using novelty, contribution, and feasibility, with a p-norm and linear normalization.The paper characterizes the resulting score as an unbiased, monotonic transformation of the true attribute means under its assumptions.Source: §4.5 · pp. 10–11
  • The HSPIM+ result assumes bounded positive scores, unbiased attribute scores independent of their confidence scores, and idealized independence among attributes.The authors note that real attributes may be correlated, for example when higher novelty reduces feasibility.Source: §4.5 · p. 10
  • The overall HSPIM+ innovation score is reported as an unbiased estimator of composite innovation in expectation under the independence assumptions.The analysis describes the score as a monotonic transformation of confidence-weighted paper-level means for novelty, contribution, and feasibility.Source: §4.5 · pp. 10–11

5. Experiment

Across four peer-review datasets, the experiments compare HSPIM with supervised and zero-shot baselines, test its components and HSPIM+, and examine generalization and scoring explanations.

5.1. Datasets

  • The evaluation uses four peer-review datasets, RMSE and MAE, supervised CNN/LSTM/Transformer baselines, and one-step zero-shot LLM baselines.Originality and soundness/correctness scores serve as proxies for novelty and practical value; scoring-rationale similarity is also evaluated.Source: §5.1 · p. 11, §5.2 · p. 11, §5.3 · p. 11

5.5.1. Zero-shot HSPIM vs. Supervised DL Models and Zero-shot LLMs

  • 35.4% (DeepSeek, 0.916 to 0.592) and 17.3% (GPT, 0.770 to 0.634) average RMSE reductions show HSPIM outperforming full-paper zero-shot prompting and supervised baselines.DeepSeek-HSPIMpruning=3 also improves performance by 6.2% over the supervised Transformer.Source: §5.5.1 · p. 12, Table 3 · p. 12

5.5.2. Zero-Shot HSPIM vs. DL Models (Transfer from COLING-2020)

  • 44.0%, 41.8%, and 33.1% average improvements over the best CNN, LSTM, and Transformer results, respectively, support zero-shot HSPIM's transfer from COLING-2020.The comparison trains deep-learning models on COLING-2020 and evaluates them on ICLR-2017, CoNLL-2016, and ACL-2017.Source: Table 4 · p. 12, §5.5.2 · p. 13

5.5.3. HSPIM Framework Comparison

  • RMSE decreases from SSPIM to naive HSPIM and then HSPIMjoint, supporting QA augmentation and further gains from genetic-algorithm prompt optimization.Results are reported on both training and test sets.Source: §5.5.3 · p. 13, Table 5 · p. 13

5.5.4. HSPIM + Results

  • HSPIM+ achieves best average RMSE 0.4533 and MAE 0.3917, versus HSPIM's 0.5915 and 0.4681; pruning has the lowest RMSE and MAE across norm choices.On ACL-2017, HSPIM+ outperforms zero-shot LLM baselines and matches supervised models.Source: §5.5.4 · p. 13, Table 6 · p. 14

5.5.5. Domain Generalization Experiments

  • On PMC, HSPIM achieves the best RMSE and MAE; on arXiv, it ranks second, outperforming zero-shot LLM baselines on both datasets.Citation counts are transformed into ground-truth innovation scores because the datasets lack peer-review scores.Source: §5.5.5 · p. 13, Table 7 · p. 14

5.6. Semantic Textual Similarity

  • HSPIMjoint's scoring rationales are more semantically similar to peer reviews than SEA-E's and SSPIM's, supporting quantitative assessment with qualitative explanations.Average cosine similarity improves by 27.3% and 7.4%, and BERTScore by 3.6% and 0.7%, respectively, versus SEA-E and SSPIM.Source: §5.6 · pp. 13–14, Table 8 · p. 14

5.7. LLM-based HSPIM Comparison

  • Mistral-7B outperforms the other tested LLMs on three of four datasets, indicating potential for innovation assessment with lightweight models.The comparison also includes LLaMA-3-8B and Qwen2.5-7B.Source: §5.7 · p. 14, Table 9 · p. 14

6. Experimental Analysis

The analyses probe how HSPIM's weighting, section handling, prompts, and LLM behaviors affect prediction, robustness, interpretability, and deployment.

6.1. Ablation Study

  • Confidence-weighted scoring reduces RMSE in SSPIM and HSPIM, while combining common and section-specific questions cuts RMSE by 13.90% and 18.43% versus either question type alone.The respective comparisons are against specific-only and common-only questions.Source: §6.1.1 · p. 15, §6.1.2 · p. 15

6.2. Analysis of Section-Based Scoring

  • HSPIMjoint has the lowest RMSE on ICLR-2017, CoNLL-2016, and COLING-2020, and beats five of nine single-section baselines on ACL-2017; section types show systematic scoring biases.Abstract and Conclusion receive higher scores with lower variance, while Related Work and Experiments receive lower scores with greater spread.Source: §6.2.1 · p. 15, §6.2.2 · p. 15, Figure 7 · p. 16

6.2.3. Modeling Section Combination

  • Several merged-section strategies improve performance over independent sections on some datasets, making section combination a potential improvement path.Examples include Approach + Experiments and Experiments + Analysis + Discussion + Conclusion.Source: §6.2.3 · p. 15

6.3. Sensitivity Analysis

  • Changing temperature from 0 to 1.0 has limited metric impact, while critical-scoring wording lowers RMSE and MAE on most datasets but not ACL-2017.The paper says this wording can help address concentrated predicted scores, but more effective strategies remain necessary.Source: §6.3.1 · p. 15, §6.3.2 · pp. 15, 17, Table 13 · p. 17

6.3.3. Sensitivity Analysis on Self-Praising Expressions

  • Neutralizing self-praising language lowers average innovation scores for Abstract + Introduction, while all-section HSPIM shows smaller changes in scores and RMSE.The analysis replaces detected emphatic expressions with neutral wording and does not assume those expressions are unreasonable.Source: §6.3.3 · p. 17, Table 15 · p. 18

6.4. Comparison of Prompt Optimization Methods

  • GA training RMSE trends downward and reaches lower values in fewer iterations than Random Search or Simulated Annealing, though the latter methods show no clear improvement trend.The methods use different numbers of prompt combinations and iterations, and RMSE may fluctuate because each iteration samples a different batch.Source: §6.4 · pp. 17–18, Figure 9 · p. 17

6.5. Analysis of Innovation Score Distributions

  • Predicted scores are narrower than ground truth; on ACL-2017 their mean and variance are 3.48 and 0.01, versus 3.97 and 0.57, while occasional section misclassification changes errors only slightly.LLMs rarely give extreme scores, and tests randomly convert 5%, 10%, or 20% of section labels to unmatched.Source: §6.5 · p. 18, §6.6 · p. 18, Figure 10 · p. 18

6.7. LLM Computational Costs

  • Mistral-7B is time-efficient among tested small models; innovation scores show a weak positive trend with citations, and selected prompts emphasize novelty evidence and balanced evaluation.The two most-cited papers both score about 3.5, despite ground-truth scores of 3.5 and 5.0; the common question asks for arguments both against and for originality.Source: §6.7 · p. 19, §6.8.1 · pp. 19–20, §6.8.2 · p. 20

7. Discussion

The discussion explains how HSPIM addresses long-text analysis, innovation scope, and generalization, then identifies limitations and deployment considerations.

7.1. Addressing Challenges in Content-Based Innovation Measurement

  • HSPIM decomposes papers into section chunks and QA pairs, then uses confidence-weighted section novelty scores to estimate paper-level innovation; QA augmentation and prompt optimization support effectiveness and generalization.The discussion cites results for the weighted strategy, QA augmentation, and zero-shot comparisons with supervised and LLM baselines.Source: §7.1 · p. 20

7.2. Limitations and Future Works •

  • HSPIM assumes papers have academic integrity; fabricated results or omitted related work may inflate innovation scores.Detecting academic dishonesty is proposed as a future direction.Source: §7.2 · p. 20
  • LLMs tend to assign moderate scores and avoid very high scores, so short sections may receive conservative evaluations.The authors attribute this possible bias to verbosity bias and plan to explore better ways to detect highly innovative papers.Source: §7.2 · p. 20
  • Evaluations may be biased when a paper appears in LLM training data, or unreliable for emerging fields outside the model's knowledge.The authors suggest online retrieval-augmented generation as a future way to improve domain knowledge.Source: §7.2 · p. 20

7.3. Practicality and Deployment •

  • HSPIM provides paper-level scores, QA pairs, and reasons for inspection; it runs end to end with API-based or open-source LLMs, and chunks can be parallelized.Reducing chunk count lowers inference cost roughly proportionally; prompt optimization is offline, so inference uses selected prompts.Source: §7.3 · pp. 20, 22

8. Conclusion

HSPIM measures scientific-paper innovation through a training-free hierarchy that decomposes papers into sections and question-answering subtasks. It targets full-paper innovation measurement and provides section-based QA pairs and scoring rationales.

8. Conclusion

  • HSPIM decomposes papers into sections and question-answering subtasks, then applies weighted innovation scoring.It classifies text chunks by section type and turns paper-level measurement into section-level QA and scoring tasks.Source: §8 · p. 22
  • Unlike approaches focused on selected sections or review-text generation, HSPIM directly measures innovation with a training-free hierarchical design.Source: §8 · p. 22
  • HSPIM provides section-based QA pairs and scoring rationales, and experiments demonstrate its effectiveness and interpretability.Source: §8 · p. 22
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