Datasets:

BFTree
/

RWGBench

language:
  - en
license: mit
size_categories:
  - 10K<n<100K
task_categories:
  - text-generation
  - text-retrieval
pretty_name: RWGBench
tags:
  - code

RWGBench is a benchmark for evaluating related work generation (RWG) through the lens of scholarly positioning and citation decision-making, rather than surface-level text similarity. It is presented in the paper RWGBench: Evaluating Scholarly Positioning in Related Work Generation.

The official repository containing evaluation scripts and baselines is available on GitHub at BFTree/RWGBench.

It includes a large-scale paper collection, a 1,091,394 retrieval corpus, 40,108 papers in computer science with full text and citation lists, a curated 100-paper test set and a fully automated evaluation framework.

Dataset Structure

File	# Entries	Description
`papers.json`	40,108	CS papers (arXiv 2020–2025) with full text and citation lists
`corpus.json`	1,091,394	Retrieval corpus — title + abstract per paper
`gold100_papers.json`	100	Quality-filtered test set with gold related work sections

corpus.json — retrieval candidates

{
  "doc_id": 1589,
  "title": "LoRA: Low-Rank Adaptation of Large Language Models",
  "abstract": "We propose a low-rank adaptation method that freezes pretrained model weights..."
}

gold100_papers.json — test papers

{
  "paper_id": 9745,
  "title": "EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models",
  "abstract": "...",
  "introduction": "...",
  "related_work": "Model quantization. Quantization is a widely employed technique...",
  "citations": [191955, 118706, 517176, 264652, 1589, 2253, ... ],
  "overall_score": 90.6
}

citations is a list of doc_ids from corpus.json. overall_score is a GLM-4 quality rating (0–100).

Sample Usage

For integration into custom pipelines using the evaluation code from the GitHub repository:

from src.evaluation.single_paper_evaluator import SinglePaperEvaluator

evaluator = SinglePaperEvaluator(
    gold_papers_path="data/gold100_papers.json",
    corpus_path="data/corpus.json",
    use_llm_judge=False,       # set True to include LLM-as-Judge scores
    llm_model="deepseek-v3",   # any OpenAI-compatible model name
)

result = evaluator.evaluate(
    paper_id=...,         # int
    generated_text=...,   # str, text with [1], [2], ... citations
    citation_list=[...],  # list of doc_id (int) or title (str); index i → [i+1]
)

Data Collection & Ethics

Source: Public arXiv metadata (2020–2025), respecting arXiv's terms of use
Copyright: Only metadata (titles, abstracts) and author-written content; no full-text.
Ethical Use: Intended for non-commercial research only