Datasets:

maybenotime
/

SIG_Wiki

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 (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

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

@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}
}