The study evaluates five large language model (LLM) review paradigms across different input formats and models on 232 ICLR 2025 submissions. Direct absolute rating aligns with human rankings but tends to over‑accept papers, while recalibration improves agreement and reduces API calls. Abstract‑only inputs outperform full‑paper summarization, and model choice is as influential as the paradigm itself, yet none of the LLM methods surpasses a simple always‑reject baseline in accuracy.
arXiv:2606. 15887v1 Announce Type: cross Abstract: Large language model (LLM) systems are increasingly proposed to assist peer review, yet most evaluations judge the prose of machine-generated review text, not the validity of the numeric score a system assigns.
arXiv:2608. 03659v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate scientific reviews, yet existing evaluations rarely examine whether different providers align with both conference decisions and human reviewing priorities within the same controlled setting.
arXiv:2607. 28282v1 Announce Type: cross Abstract: Evaluating the quality and relevance of textual outputs from Large Language Models (LLMs) remains challenging and resource-intensive.
arXiv:2611.06789v1 Announce Type: cross Abstract: A fast acceptance classifier could select revisions for a slower evaluator. We ask whether agreement on existing papers supports that use. We compare...
arXiv:2609.23264v1 Announce Type: new Abstract: Peer-review evaluation is increasingly being automated with LLM-as-a-judge metrics, but this creates a measurement risk. A review may receive a high sc...
arXiv:2608.21374v1 Announce Type: new Abstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspec...
The paper introduces a method to mitigate reviewer lottery error in scientific peer review by generating checklists of criticisms from historical review data. These checklists are organized into a tree of coarsenings, and the optimal coarsening is chosen to maximize correlated agreement among reviewers. Experiments on NeurIPS 2021 and ICLR 2022 data show that the learned checklists significantly reduce mean squared error in simulated and human review settings.
arXiv:2608.28626v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate peer reviews, prompting examination of their capacity for critical evaluation. This st...
arXiv:2609.05947v1 Announce Type: new Abstract: Peer review is central to quality control in science. However, existing evaluations of AI-assisted peer review mainly focus on the overall quality of g...
arXiv:2606. 13221v2 Announce Type: replace Abstract: Evaluating new large language models typically requires costly human annotation campaigns at scale.
Large Language Model judges are commonly used to rank texts via pairwise comparison, with reliability traditionally measured by position bias, transitivity, and pairwise agreement. This paper argues that these proxies are misleading because they are dominated by close‑rank‑gap pairs, which contribute little to the overall ranking, while far‑gap pairs carry the true ranking signal. Experiments on simulations and human‑rated corpora show weak correlation between the proxies and actual ranking accuracy, suggesting judges should be evaluated using rank‑gap‑conditional metrics against human rankings.
The paper introduces a verification‑centric framework and benchmark for evaluating large language models (LLMs) as assistants in peer review, focusing on error detection rather than imitation of human reviews. It presents a scalable benchmark that injects synthetic errors into conference papers to test LLMs’ ability to spot logical contradictions, and proposes a Multi‑Layered Review (MLR) framework that emphasizes deep manuscript comprehension before generating reviews. Experiments show the approach aligns well with human review scores, excels at error detection, and offers complementary reviewer insights, while also revealing vulnerabilities to adversarial manipulation.