{
  "annotation_id": "bcf46c13-a9af-4950-b804-e41110da9c6b",
  "date_created": "2026-02-17T05:53:20.075000Z",
  "date_modified": "2026-02-17T05:53:20.075000Z",
  "file_hash": "1a025b73b752cc3dd2c313c422c0b8c157f6a0ad3748d0d86baaa2dea359121c",
  "private": false,
  "record": {
    "abstract": "The rapid expansion of AI research has intensified the Reviewer Gap, threatening the peer-review sustainability and perpetuating a cycle of low-quality evaluations. This position paper critiques existing LLM approaches that automatically generate reviews and argues for a paradigm shift that positions LLMs as tools for assisting and educating human reviewers. We define the core principles of high-quality peer review and propose two complementary systems grounded in these foundations: (i) an LLM-assisted mentoring system that cultivates reviewers\u0027 long-term competencies, and (ii) an LLM-assisted feedback system that helps reviewers refine the quality of their reviews. This human-centered approach aims to strengthen reviewer expertise and contribute to building a more sustainable scholarly ecosystem.",
    "arxiv_id": "2601.09182",
    "authors": [
      "JungMin Yun",
      "JuneHyoung Kwon",
      "MiHyeon Kim",
      "YoungBin Kim"
    ],
    "categories": [
      "cs.AI",
      "cs.CY",
      "cs.HC"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Position on LLM-Assisted Peer Review: Addressing Reviewer Gap through Mentoring and Feedback",
    "url": "https://arxiv.org/abs/2601.09182",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "ba5a4932-cfc7-4c70-a39a-b83c51741bc2",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}