README.md

---
license: mit
language:
- en
tags:
- science
- accelerator-physics
- particle-accelerator
pretty_name: Accel-IR
size_categories:
- 1K<n<10K
task_categories:
- text-retrieval
- question-answering
configs:
- config_name: expert_core
  data_files:
  - split: test
    path: "Accel_IR_expert_core.csv"
- config_name: augmented
  data_files:
  - split: test
    path: "Accel_IR_augmented.csv"
---

# Accel-IR Benchmark: A Gold Standard for Particle Accelerator Physics

This repository contains the **Accel-IR Benchmark**, a domain-specific Information Retrieval (IR) dataset for particle accelerator physics. It was developed as part of the Master's Thesis *"From Dataset to Optimization: A Benchmarking Framework for Information Retrieval in the Particle Accelerator Domain"* by **Qing Dai** (University of Zurich, 2025), in collaboration with the **Paul Scherrer Institute (PSI)**.


## Dataset Configurations

This benchmark is available in two configurations. You can load specific versions based on your evaluation needs:

| Configuration | Rows | Description | Use Case |
| :--- | :--- | :--- | :--- |
| **`expert_core`** | 390 | Purely **expert-annotated** pairs. Labeled by 7 domain experts (PhDs/Researchers) from PSI.  | Precise evaluation against human ground truth. |
| **`augmented`** | 1,357 | The `expert_core` + **curated hard negatives**. The negatives were generated using a expert-validated automatic annotation pipeline. | Realistic IR evaluation with many more negatives than positives. |

## Dataset Structure

### Data Fields
Each row in the dataset represents a query-document pair with the following columns:
- **`Source`**:  The referenced paper or an IPAC publication, source of the chunks.
- **`Question`**: The domain-specific scientific question.
- **`Answer`**: Answer to the question.
- **`Question_type`**: The category of the question, simulating diverse information needs:
  - `Fact`: Specific details or parameters.
  - `Definition`: Explanations of concepts/terms.
  - `Reasoning`: Logic behind phenomena or mechanisms.
  - `Summary`: Key points or conclusions.
- **`Referenced_file(s)`**: Referenced papers for the questions, provided by experts.
- **`chunk_text`**: The text passage retrieved from domain-expert-referenced papers or IPAC conference papers.
- **`expert_annotation`** *(Core only)*: The raw relevance score given by domain experts on a 5-point Likert scale:
  - `1`: Irrelevant
  - `2`: Partially Irrelevant
  - `3`: Hard to Decide (Excluded from Core)
  - `4`: Partially Relevant
  - `5`: Relevant
- **`specific to paper`**: Indicates if the question is "Context-Dependent" (answerable *only* by the referenced paper) or "General" (answerable by broader domain knowledge).
- **`Label`**: The binary ground truth used for evaluation metrics (nDCG/MAP).
  - `1` (Relevant): Derived from expert scores 4 & 5.
  - `0` (Irrelevant): Derived from expert scores 1 & 2, or pipeline hard negatives.
  
## Creation Methodology

1.  **Expert Core**:
    -   Created by **7 domain experts** from the Electron Beam Instrumentation Group at PSI.
    -   Experts reviewed query-chunk pairs and annotated them on a 1-5 scale using a custom interface.
    -   Pairs labeled as "3 - Not Sure" were removed to ensure no ambiguity.

2.  **Augmentation (Hard Negatives)**:
    -   To simulate realistic retrieval scenarios where negatives far outnumber positives, the core dataset was augmented.
    -   **Hard Negatives** were generated using an expert-validated automatic annotation pipeline.

## Usage

You can load the datasets using the Hugging Face `datasets` library.

### Load the Expert Core (390 pairs)
```python
from datasets import load_dataset

# Load the pure expert-annotated subset
ds_core = load_dataset("qdai/Accel-IR", "expert_core", split="test")

print(ds_core[0])
```


## Citation
If you use this dataset, please cite:
> Qing Dai, "From Dataset to Optimization: A Benchmarking Framework for Information Retrieval in the Particle Accelerator Domain", Master's Thesis, University of Zurich, 2025.