1. Automatically Annotating Articles Towards Opening and Reusing Transparent Peer Reviews

   http://arxiv.org/abs/1812.01027v1 · arxiv

   An increasing number of scientific publications are created in open and transparent peer review models: a submission is published first, and then reviewers are invited, or a submission is reviewed in a closed environment but then these reviews are published with the final article, or combinations of

2. NoReC: The Norwegian Review Corpus

   http://arxiv.org/abs/1710.05370v1 · arxiv

   This paper presents the Norwegian Review Corpus (NoReC), created for training and evaluating models for document-level sentiment analysis. The full-text reviews have been collected from major Norwegian news sources and cover a range of different domains, including literature, movies, video games, re

3. Large Language Models and Video Games: A Preliminary Scoping Review

   http://arxiv.org/abs/2403.02613v1 · arxiv

   Large language models (LLMs) hold interesting potential for the design, development, and research of video games. Building on the decades of prior research on generative AI in games, many researchers have sped to investigate the power and potential of LLMs for games. Given the recent spike in LLM-re

4. Aspect-Guided Multi-Level Perturbation Analysis of Large Language Models in Automated Peer Review

   http://arxiv.org/abs/2502.12510v1 · arxiv

   We propose an aspect-guided, multi-level perturbation framework to evaluate the robustness of Large Language Models (LLMs) in automated peer review. Our framework explores perturbations in three key components of the peer review process-papers, reviews, and rebuttals-across several quality aspects,

5. AI-Assisted Peer Review Across Research Communities: From Reviewer AI Policies to LLM Review Quality

   http://arxiv.org/abs/2608.03581v1 · arxiv

   AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are. We address these questions by first surveying

6. Effectiveness of Anonymization in Double-Blind Review

   http://arxiv.org/abs/1709.01609v1 · arxiv

   Double-blind review relies on the authors' ability and willingness to effectively anonymize their submissions. We explore anonymization effectiveness at ASE 2016, OOPSLA 2016, and PLDI 2016 by asking reviewers if they can guess author identities. We find that 74%-90% of reviews contain no correct gu

7. Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions

   http://arxiv.org/abs/2406.05688v1 · arxiv

   Large Language Models (LLMs) have demonstrated wide-ranging applications across various fields and have shown significant potential in the academic peer-review process. However, existing applications are primarily limited to static review generation based on submitted papers, which fail to capture t

8. Review on ferroelectric/polar metals

   http://arxiv.org/abs/2007.11200v1 · arxiv

   The possibility of reconciliation between seemingly mutually exclusive properties in one system can not only lead to theoretical breakthroughs but also potential novel applications. The research on the coexistence of two purportedly contra-indicated properties, ferroelectricity/polarity and conducti

9. Previously on... Automating Code Review

   http://arxiv.org/abs/2508.18003v1 · arxiv

   Modern Code Review (MCR) is a standard practice in software engineering, yet it demands substantial time and resource investments. Recent research has increasingly explored automating core review tasks using machine learning (ML) and deep learning (DL). As a result, there is substantial variability

10. Optimizing Peer Grading: A Systematic Literature Review of Reviewer Assignment Strategies and Quantity of Reviewers

    http://arxiv.org/abs/2508.11678v2 · arxiv

    Peer assessment has established itself as a critical pedagogical tool in academic settings, offering students timely, high-quality feedback to enhance learning outcomes. However, the efficacy of this approach depends on two factors: (1) the strategic allocation of reviewers and (2) the number of rev

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