REVIEW 7 cited by
Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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 the dynamic and iterative nature of real-world peer reviews. In this paper, we reformulate the peer-review process as a multi-turn, long-context dialogue, incorporating distinct roles for authors, reviewers, and decision makers. We construct a comprehensive dataset containing over 26,841 papers with 92,017 reviews collected from multiple sources, including the top-tier conference and prestigious journal. This dataset is meticulously designed to facilitate the applications of LLMs for multi-turn dialogues, effectively simulating the complete peer-review process. Furthermore, we propose a series of metrics to evaluate the performance of LLMs for each role under this reformulated peer-review setting, ensuring fair and comprehensive evaluations. We believe this work provides a promising perspective on enhancing the LLM-driven peer-review process by incorporating dynamic, role-based interactions. It aligns closely with the iterative and interactive nature of real-world academic peer review, offering a robust foundation for future research and development in this area. We open-source the dataset at https://github.com/chengtan9907/ReviewMT.
Forward citations
Cited by 7 Pith papers
-
Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review
Introduces Re3Align dataset of review-response-revision triplets, REspGen author-in-the-loop generation framework, and REspEval multi-metric suite for controllable peer-review response generation.
-
AbGen: Evaluating Large Language Models in Ablation Study Design and Evaluation for Scientific Research
A benchmark of 1,500 expert-annotated ablation study designs from 807 NLP papers shows frontier LLMs underperform human experts and that LLM-as-a-judge evaluations correlate weakly with human judgments.
-
SketchAgent: Generating Structured Diagrams from Hand-Drawn Sketches
SketchAgent automates sketch-to-diagram conversion with a three-agent pipeline, but its benchmark replaces hand-drawn sketches with simplified renderings of the very diagrams the system must produce.
-
Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers
LIMITGEN evaluates LLMs on identifying paper limitations and shows strong models still miss roughly half of obvious flaws, with retrieval augmentation giving modest but consistent gains.
-
Detailed radial scale height profile of dust grains as probed by dust self-scattering in HL Tau
From the near-far side asymmetry and azimuthal contrast in HL Tau's polarized intensity, the authors infer a radial dust scale height profile and a turbulence parameter alpha increasing from 1e-5 at 100 au to 1e-2.5 at 20 au.
-
AI for Auto-Research: Roadmap & User Guide
The paper delivers a stage-by-stage roadmap for AI in research, showing reliable assistance in retrieval and tool tasks but fragility in novelty and judgment, advocating human-governed collaboration.
-
How Far Are AI Scientists from Changing the World?
This survey proposes a four-level capability framework for AI Scientist systems and, using an AI reviewer, finds that current systems produce papers rated well below normal scientific standards.
Discussion (0). Sign in to comment.