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HFEPX · Eval paper review

ReviewScore: Misinformed Peer Review Detection with Large Language Models

Hyun Ryu, Doohyuk Jang, Hyemin S. Lee, Joonhyun Jeong +15 more

Published

Sep 25, 2025

Citations

0

Trust level

Low

Usefulness score

15/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Domain Experts

Signals refreshed

Mar 18, 2026

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Tags

Automatic Metrics

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Validate the exact study setup in the full paper before operational use.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
15/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes. To reliably detect low-quality reviews, we define misinformed review points as either "weaknesses" in a review that contain incorrect premises, or "questions" in a review that can be already answered by the paper. We verify that 15.2% of weaknesses and 26.4% of questions are misinformed and introduce ReviewScore indicating if a review point is misinformed. To evaluate the factuality of each premise of weaknesses, we propose an automated engine that reconstructs every explicit and implicit premise from a weakness. We build a human expert-annotated ReviewScore dataset to check the ability of LLMs to automate ReviewScore evaluation. Then, we measure human-model agreements on ReviewScore using eight current state-of-the-art LLMs. The models show F1 scores of 0.4--0.5 and kappa scores of 0.3--0.4, indicating moderate agreement but also suggesting that fully automating the evaluation remains challenging. A thorough disagreement analysis reveals that most errors are due to models' incorrect reasoning. We also prove that evaluating premise-level factuality shows significantly higher agreements than evaluating weakness-level factuality.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes."

Quality Controls

partial

Inter Annotator Agreement Reported

Calibration/adjudication style controls detected.

"Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes."

Reported Metrics

partial

F1, Kappa, Agreement

Useful for evaluation criteria comparison.

"Then, we measure human-model agreements on ReviewScore using eight current state-of-the-art LLMs."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"We build a human expert-annotated ReviewScore dataset to check the ability of LLMs to automate ReviewScore evaluation."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

f1kappaagreement
Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Domain Experts
Expertise required
General
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
None
Quality controls
Inter Annotator Agreement Reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes.
  • To reliably detect low-quality reviews, we define misinformed review points as either "weaknesses" in a review that contain incorrect premises, or "questions" in a review that can be already answered by the paper.
  • We verify that 15.2% of weaknesses and 26.4% of questions are misinformed and introduce ReviewScore indicating if a review point is misinformed.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Expert verification evaluation F1

Contribution summary

  • To evaluate the factuality of each premise of weaknesses, we propose an automated engine that reconstructs every explicit and implicit premise from a weakness.
  • We build a human expert-annotated ReviewScore dataset to check the ability of LLMs to automate ReviewScore evaluation.
  • The models show F1 scores of 0.4--0.5 and kappa scores of 0.3--0.4, indicating moderate agreement but also suggesting that fully automating the evaluation remains challenging.

Why it matters for eval

  • We build a human expert-annotated ReviewScore dataset to check the ability of LLMs to automate ReviewScore evaluation.
  • The models show F1 scores of 0.4--0.5 and kappa scores of 0.3--0.4, indicating moderate agreement but also suggesting that fully automating the evaluation remains challenging.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    Detected: Inter Annotator Agreement Reported

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    Detected: f1, kappa, agreement