---
license: cc-by-sa-4.0
task_categories:
- question-answering
language:
- en
tags:
- rag
- retrieval-augmented-generation
- robustness
- spurious-features
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# SIG_Wiki

**SIG_Wiki** (Spurious features In Golden documents from Wiki) is a lightweight benchmark for
evaluating the robustness of Retrieval-Augmented Language Models (RALMs) against **spurious
features** — semantic-agnostic modifications to grounding documents that should not change the
answer, but often do.

It is the challenge subset of the `SURE_Wiki` dataset from the paper
[*Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious
Features in Grounding Data*](https://aclanthology.org/2026.acl-long.1545/) (ACL 2026). Each entry is
a hard case where both Mistral-7B-Instruct-v0.3 and Llama-3.1-8B-Instruct were non-robust. Queries
come from NQ-open; documents come from a Wikipedia dump (retrieved with Contriever-msmarco).

Code: https://github.com/maybenotime/RAG-SpuriousFeatures

## Structure

1,600 instances = 5 feature types × their perturbations × 100 instances each.

| `feature_type` | `perturbation` values | rows |
|---|---|---|
| `format` | `format_preference_html`, `_json`, `_markdown`, `_yaml` | 400 |
| `style`  | `simple_preference_llm`, `complex_preference_llm` | 200 |
| `logic`  | `logic_preference_reverse`, `_random`, `_llm` | 300 |
| `source` | `source_preference_llm`, `self_preference_mistral_7b`, `self_preference_llama3_8b` | 300 |
| `meta`   | `metadata_timestamp_pre`, `_post`, `metadata_datasource_wiki`, `_twitter` | 400 |

### Fields

- `feature_type` — spurious feature category.
- `perturbation` — the specific perturbation applied.
- `question` — the query (NQ-open).
- `answers` — list of acceptable gold answers.
- `title` — document title.
- `original_text` — the un-perturbed golden document.
- `perturbed_doc` — the same document after the spurious feature is injected.

The raw nested JSON (grouped by feature type / perturbation) is also available at
`raw/SIG_benchmark.json`.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("maybenotime/SIG_Wiki", split="test")
fmt = ds.filter(lambda r: r["feature_type"] == "format")
```

Feed `original_text` and `perturbed_doc` (with the same `question`) to a RALM and compare the
correctness of the two answers. Following the paper, report the instance-level **Robustness Rate /
Win Rate / Lose Rate**.

## Citation

```bibtex
@inproceedings{yang2026sure,
  title     = {Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data},
  author    = {Yang, Shiping and Wu, Jie and Ding, Wenbiao and Wu, Ning and Liang, Shining and Gong, Ming and Li, Hongzhi and Zhang, Hengyuan and Chang, Angel X. and Zhang, Dongmei},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year      = {2026},
  pages     = {33479--33499}
}
```