From Isolated Scoring to Collaborative Ranking: A Comparison-Native Framework for LLM-Based Paper Evaluation

• URL: http://arxiv.org/abs/2603.17588v1
• Date: Wed, 18 Mar 2026 10:55:02 GMT
• Title: From Isolated Scoring to Collaborative Ranking: A Comparison-Native Framework for LLM-Based Paper Evaluation
• Abstract summary: Large language models (LLMs) are currently applied to scientific paper evaluation by assigning an absolute score to each paper independently.<n>We propose shifting paper evaluation from isolated scoring to collaborative ranking.<n>Our framework achieves an average relative improvement of textbf21.8% over the strong baseline DeepReview-14B.
• Score: 12.13840753234467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are currently applied to scientific paper evaluation by assigning an absolute score to each paper independently. However, since score scales vary across conferences, time periods, and evaluation criteria, models trained on absolute scores are prone to fitting narrow, context-specific rules rather than developing robust scholarly judgment. To overcome this limitation, we propose shifting paper evaluation from isolated scoring to collaborative ranking. In particular, we design \textbf{C}omparison-\textbf{N}ative framework for \textbf{P}aper \textbf{E}valuation (\textbf{CNPE}), integrating comparison into both data construction and model learning. We first propose a graph-based similarity ranking algorithm to facilitate the sampling of more informative and discriminative paper pairs from a collection. We then enhance relative quality judgment through supervised fine-tuning and reinforcement learning with comparison-based rewards. At inference, the model performs pairwise comparisons over sampled paper pairs and aggregates these preference signals into a global relative quality ranking. Experimental results demonstrate that our framework achieves an average relative improvement of \textbf{21.8\%} over the strong baseline DeepReview-14B, while exhibiting robust generalization to five previously unseen datasets. \href{https://github.com/ECNU-Text-Computing/ComparisonReview}{Code}.

Related papers

• Prompt Perturbation for Reliable LLM Evaluation over Comparison Graphs [10.095945693317079]
  evaluating large language models (LLMs) is important for understanding their capabilities, comparing competing systems, and supporting the deployment of reliable models in practice.<n>For open-ended tasks, pairwise evaluation has become a popular paradigm, in which two responses to the same prompt are compared and the resulting judgments are aggregated into an overall ranking.<n>A central challenge of this paradigm is intransitivity: the induced comparison outcomes may fail to support any coherent global ranking.
  arXiv  Detail & Related papers  (2026-06-16T07:44:45Z)
• NAIPv2: Debiased Pairwise Learning for Efficient Paper Quality Estimation [58.30936615525824]
  We present NAIPv2, a debiased and efficient framework for paper quality estimation.<n> NAIPv2 employs pairwise learning within domain-year groups to reduce inconsistencies in reviewer ratings.<n>It is trained on pairwise comparisons but enabling efficient pointwise prediction at deployment.
  arXiv  Detail & Related papers  (2025-09-29T17:59:23Z)
• Direct-Scoring NLG Evaluators Can Use Pairwise Comparisons Too [42.13843953705695]
  We propose a direct-scoring method which uses synthetic summaries to act as pairwise machine rankings at test time.<n>We show that our method performs comparably to state-of-the-art pairwise evaluators in terms of axis-averaged sample-level correlations.
  arXiv  Detail & Related papers  (2025-09-05T18:48:34Z)
• AllSummedUp: un framework open-source pour comparer les metriques d'evaluation de resume [2.2153783542347805]
  This paper investigates challenges in automatic text summarization evaluation.<n>Based on experiments conducted across six representative metrics, we highlight significant discrepancies between reported performances in the literature and those observed in our experimental setting.<n>We introduce a unified, open-source framework, applied to the SummEval dataset and designed to support fair and transparent comparison of evaluation metrics.
  arXiv  Detail & Related papers  (2025-08-29T08:05:00Z)
• Leveraging Reference Documents for Zero-Shot Ranking via Large Language Models [16.721450557704767]
  RefRank is a simple and effective comparative ranking method based on a fixed reference document.<n>We show that RefRank significantly outperforms Pointwise baselines and could achieve performance at least on par with Pairwise approaches.
  arXiv  Detail & Related papers  (2025-06-13T04:03:09Z)
• CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation [87.44350003888646]
  Eval-Instruct can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting. CritiqueLLM is empirically shown to outperform ChatGPT and all the open-source baselines.
  arXiv  Detail & Related papers  (2023-11-30T16:52:42Z)
• FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets [69.91340332545094]
  We introduce FLASK, a fine-grained evaluation protocol for both human-based and model-based evaluation. We experimentally observe that the fine-graininess of evaluation is crucial for attaining a holistic view of model performance.
  arXiv  Detail & Related papers  (2023-07-20T14:56:35Z)
• COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization [84.70895015194188]
  We propose a Contrastive Learning based re-ranking framework for one-stage summarization called COLO. COLO boosts the extractive and abstractive results of one-stage systems on CNN/DailyMail benchmark to 44.58 and 46.33 ROUGE-1 score.
  arXiv  Detail & Related papers  (2022-09-29T06:11:21Z)
• Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries [59.27273928454995]
  Current pre-trained models applied to summarization are prone to factual inconsistencies which misrepresent the source text or introduce extraneous information. We create a crowdsourcing evaluation framework for factual consistency using the rating-based Likert scale and ranking-based Best-Worst Scaling protocols. We find that ranking-based protocols offer a more reliable measure of summary quality across datasets, while the reliability of Likert ratings depends on the target dataset and the evaluation design.
  arXiv  Detail & Related papers  (2021-09-19T19:05:00Z)
• Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation [81.55533657694016]
  We propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation. Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and inter-review encoder (level three) We are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers.
  arXiv  Detail & Related papers  (2020-11-02T08:07:50Z)
• Evaluating Text Coherence at Sentence and Paragraph Levels [17.99797111176988]
  We investigate the adaptation of existing sentence ordering methods to a paragraph ordering task. We also compare the learnability and robustness of existing models by artificially creating mini datasets and noisy datasets. We conclude that the recurrent graph neural network-based model is an optimal choice for coherence modeling.
  arXiv  Detail & Related papers  (2020-06-05T03:31:49Z)

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.