Datasets:

tswj
/

llmjre-rag-eval

language:
  - en
pretty_name: LLMJRE-RAG-Eval
license: apache-2.0
task_categories:
  - text-classification
  - text-generation
task_ids:
  - text-scoring
  - text-classification
tags:
  - llm-as-a-judge
  - academic-peer-review
  - retrieval-augmented-generation
  - rag
  - rebuttal
  - meta-review
  - benchmark
  - evaluation
  - education
  - peer-review

LLMJRE-RAG-Eval

Retrieval-Augmented LLM Reviewers for Academic Peer Review: Improving Human Alignment and Rebuttal-Aware Evaluation

LLMJRE-RAG-Eval is a benchmark dataset for evaluating heterogeneous Large Language Model (LLM) reviewers across the complete academic peer-review workflow. The benchmark supports research on LLM-as-a-Judge for academic paper assessment by providing structured datasets for reviewer evaluation, author rebuttals, meta-review generation, and retrieval-augmented reviewer guidance.

The benchmark extends the Re² peer-review dataset by introducing a Retrieval-Augmented Generation (RAG) evaluation framework that incorporates conference-specific reviewer guidelines into the review process. It enables systematic evaluation of whether external reviewer guidance improves agreement between LLM-generated evaluations and human reviewer assessments.

Dataset Summary

LLMJRE-RAG-Eval consists of two benchmark tasks corresponding to the complete conference peer-review lifecycle.

Review Benchmark
- Initial manuscript review
- Human review alignment
- Overall recommendation
Rebuttal Benchmark
- Author rebuttal
- Rebuttal-aware review
- Reviewer belief revision
- Final recommendation

The benchmark supports four research questions:

RQ1: To what extent do heterogeneous LLM reviewers align with human reviewer evaluations of academic papers?
RQ2: How do heterogeneous LLM reviewers differ in their evaluation behaviour and response to rebuttal information?
RQ3: Does retrieval-augmented review guidance improve the alignment between LLM-generated evaluations and human reviewer scores?
RQ4: Does retrieval-augmented review guidance improve belief-shift accuracy after author rebuttals?

Dataset Structure

llmjre-rag-eval
│
├── review
│   ├── llmjre_review.jsonl
│   └── llmjre_review.csv
│
├── rebuttal
│   ├── llmjre_rebuttal.jsonl
│   └── llmjre_rebuttal.csv
│
├── sample
│   ├── llmjre_review_sample_1000.jsonl
│   ├── llmjre_review_sample_1000.csv
│   ├── llmjre_rebuttal_sample_1000.jsonl
│   ├── llmjre_rebuttal_sample_1000.csv
│   ├── sample_inference_report.json
│   └── unique_conference_year_type.csv
│
├── metadata
│   ├── benchmark_schema.json
│   ├── benchmark_statistics.json
│   └── unique_conferences.csv
│
└── rag
    ├── guideline_collection_tracker.csv
    └── official_guideline_tracker.csv

Dataset Components

Review Benchmark

The review benchmark contains the information required to evaluate initial manuscript assessment and review alignment.

Typical fields include:

Paper metadata
Manuscript text
Human review comments
Human review scores
Human recommendations
Conference metadata

Rebuttal Benchmark

The rebuttal benchmark extends the review benchmark by incorporating author rebuttals and revised reviewer assessments.

Additional fields include:

Author rebuttal
Final reviewer comments
Final reviewer scores
Reviewer belief shifts
Final recommendations

Sample Benchmark

The sample benchmark contains the exact 1,000-paper evaluation subset used in the accompanying paper.

Researchers may use this subset to reproduce the published experiments and statistical analyses.

Metadata

Supporting metadata includes:

Benchmark schema
Benchmark statistics
Conference metadata
Conference distributions

RAG Metadata

The RAG directory contains metadata describing the conference-specific reviewer guideline collection used for retrieval augmentation.

It includes:

guideline collection tracker
official reviewer guideline tracker

These files document the provenance and coverage of the conference reviewer guidelines used during retrieval.

Benchmark Construction

The benchmark is derived from the Re² academic peer-review dataset and preserves the complete conference review workflow, including:

Manuscripts
Human reviewer assessments
Author rebuttals
Final reviewer decisions

LLMJRE-RAG-Eval extends the benchmark by incorporating conference-specific reviewer guidelines to support retrieval-augmented reviewer evaluation.

Recommended Tasks

The benchmark supports research in:

LLM-as-a-Judge
Academic peer review
Retrieval-Augmented Generation (RAG)
Human-AI collaboration
Meta-review generation
Rebuttal-aware evaluation
Reviewer behaviour analysis
AI-assisted scholarly communication

Loading the Dataset

The benchmark can be loaded directly from the JSONL or CSV files.

Example (JSONL):

import json

with open("review/llmjre_review.jsonl") as f:
    for line in f:
        sample = json.loads(line)
        print(sample["paper_id"])

Example (Pandas):

import pandas as pd

df = pd.read_csv("review/llmjre_review.csv")
print(df.head())

Citation

The benchmark builds upon the Re² dataset. Please also cite the original Re² paper.

@article{zhang2025re,
  title={Re$^2$: A Consistency-ensured Dataset for Full-stage Peer Review and Multi-turn Rebuttal Discussions},
  author={Zhang, Daoze and Bao, Zhijian and Du, Sihang and Zhao, Zhiyi and Zhang, Kuangling and Bao, Dezheng and Yang, Yang},
  journal={arXiv preprint arXiv:2505.07920},
  volume={abs/2505.07920},
  pages={1--15},
  year={2025}
}

Acknowledgements

LLMJRE-RAG-Eval extends the Re² benchmark by introducing retrieval-augmented conference-specific reviewer guidance for evaluating human alignment and rebuttal-aware assessment.

We thank the authors of the Re² dataset for making these research resources publicly available.

License

The benchmark is released under the Apache License 2.0, consistent with the original Re² dataset. Users should additionally comply with the licensing terms of the original Re² dataset and any applicable terms associated with the referenced conference reviewer guideline sources.