LimitGen

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Data and Code for ACL 2025 Paper "Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers"

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<p align="center">
  <h1 style="display: inline;">
    Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers
  </h1>
</p>

<p align="center">
  <a href="https://arxiv.org/abs/">📖 Paper</a> •
  <a href="https://huggingface.co/datasets/yale-nlp/">🤗 Data</a>
</p>

## 📰 News
- **2025-07-03**: We are excited to release the LimitGen paper, dataset, and code!

## LimitGen Benchmark

While LLMs show promise in various scientific tasks, their potential to assist with peer review, particularly in identifying paper limitations, remains understudied. LimitGen, the first comprehensive benchmark for evaluating LLMs' capability to support early-stage feedback and complement human peer review. Our benchmark consists of two subsets: LimitGen-Syn, a synthetic dataset carefully created through controlled perturbations of papers, and LimitGen-Human, a collection of real human-written limitations.

## 🚀 Quickstart
### 1. Setup
Install the required packages
```bash
pip install -r requirements.txt
```

### 2. Limitation Generation
As detailed in Section 5.1 of our paper, we evaluate the performance of four frontier LLMs on our benchmark. To run the experiments on different subsets, navigate to the `identification` directory and execute:

```bash
cd identification
python main_human.py    # for the human subset
# or
python main_syn.py --error_type <error_type>     # for the synthetic subset
```
Make sure to modify the data paths, specify the model name and API key, and choose whether to enable RAG before running.

### 3. RAG
1. Run the following scripts in order to obtain a list of relevant papers:
```
   cd retrieval
   python query_gen.py
   python search.py
   python recommendation.py
   python rerank.py
```

This process will generate a list of recommended papers. You will need to download the PDFs.

2. (Optional) Use MMDA to preprocess the downloaded PDFs.

3. After downloading and preprocessing the papers, run:

```
   python section_locate.py
   python rewrite.py
   ```

These scripts will generate reference content based on the selected papers.

### 4. Evaluation
1. To evaluate the LimitGen-Syn subset, follow the steps below:

```
  cd evaluation/syn
  python aspect_check.py --error_type <error_type>
  python subtype_classification.py --error_type <error_type>
  python coarse_accuracy.py
  python rating.py --error_type <error_type>
```

2. To evaluate the LimitGen-Human subset, follow the steps below:
   
```
  cd evaluation/human
  python measure_overlap.py
  python match_calculate.py
  python rating.py
```

Make sure to modify the data paths, specify the model name, API key, and other necessary configurations.

## ✍️ Citation
If you use our work and are inspired by our work, please consider cite us (available soon):
```
@inproceedings{xu-etal-2025-llms-identify,
    title = "Can {LLM}s Identify Critical Limitations within Scientific Research? A Systematic Evaluation on {AI} Research Papers",
    author = "Xu, Zhijian  and
      Zhao, Yilun  and
      Patwardhan, Manasi  and
      Vig, Lovekesh  and
      Cohan, Arman",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.1009/",
    pages = "20652--20706",
    ISBN = "979-8-89176-251-0",
    abstract = "Peer review is fundamental to scientific research, but the growing volume of publications has intensified the challenges of this expertise-intensive process. While LLMs show promise in various scientific tasks, their potential to assist with peer review, particularly in identifying paper limitations, remains understudied. We first present a comprehensive taxonomy of limitation types in scientific research, with a focus on AI. Guided by this taxonomy, for studying limitations, we present LimitGen, the first comprehensive benchmark for evaluating LLMs' capability to support early-stage feedback and complement human peer review. Our benchmark consists of two subsets: LimitGen-Syn, a synthetic dataset carefully created through controlled perturbations of high-quality papers, and LimitGen-Human, a collection of real human-written limitations. To improve the ability of LLM systems to identify limitations, we augment them with literature retrieval, which is essential for grounding identifying limitations in prior scientific findings. Our approach enhances the capabilities of LLM systems to generate limitations in research papers, enabling them to provide more concrete and constructive feedback."
}
```

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