---
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](https://huggingface.co/papers/2606.24894).

The official repository containing evaluation scripts and baselines is available on GitHub at [BFTree/RWGBench](https://github.com/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    |

<details>
<summary>corpus.json &nbsp;—&nbsp; retrieval candidates</summary>
<br>


```json
{
  "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..."
}
```

</details>

<details>
<summary>gold100_papers.json &nbsp;—&nbsp; test papers</summary>
<br>


```json
{
  "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_id`s from `corpus.json`. `overall_score` is a GLM-4 quality rating (0–100).
</details>

## Sample Usage

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

```python
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](https://arxiv.org/help/api/tou)
- **Copyright**: Only metadata (titles, abstracts) and author-written content; no full-text.
- **Ethical Use**: Intended for non-commercial research only