license: cc-by-4.0
language:
- en
tags:
- evaluation
- semantic-equivalence
- robustness
- natural-language-understanding
pretty_name: Semantic Sensitivity Benchmark
Semantic Sensitivity Benchmark
A benchmark of 10,843 equivalence groups designed to measure how consistently large language models answer semantically equivalent questions phrased in different surface forms.
Each group contains 2–5 restatements of the same question with the same correct answer. The benchmark spans 39 question categories across factual retrieval and logical reasoning.
The dataset is procedurally generated by scripts/generate_ss_groups.py (seed 42) and is fully reproducible from the accompanying code release.
Dataset structure
Each entry in ss_groups.json is a JSON object with the following fields:
| Field | Type | Description |
|---|---|---|
id |
int | Unique group identifier |
category |
str | Question category (see below) |
answer_type |
str | "yes_no" or "word" |
questions |
list[str] | 2–5 phrasings of the same question |
expected |
str | Ground-truth answer |
Example
{
"id": 0,
"category": "capital_word_order",
"answer_type": "yes_no",
"expected": "yes",
"questions": [
"Is Tirana the capital of Albania?",
"Is the capital of Albania Tirana?",
"Does Albania have Tirana as its capital?"
]
}
Categories
Factual
| Category | Groups | Description |
|---|---|---|
capital_word_order |
200 | Capital–country word-order variants |
capital_retrieval |
100 | Open-ended capital retrieval |
active_passive |
100 | Active vs. passive voice restatements |
contrastive_negation |
100 | Contrastive negation restatements |
geographic_containment |
100 | City/country containment questions |
chemical_formula |
100 | Chemical formula identification |
classification |
100 | Taxonomy / type-of questions |
element_symbol |
50 | Periodic-table symbol retrieval |
country_language |
79 | Official language of a country |
country_currency |
71 | Currency of a country |
continent |
85 | Continent of a country |
largest_city |
61 | Largest city of a country |
Logical
| Category | Groups | Description |
|---|---|---|
arithmetic_order |
400 | Comparison of two numbers (word-order variants) |
arithmetic_large |
300 | Large-number arithmetic (> 10 000) |
arithmetic_xlarge |
300 | Extra-large-number arithmetic |
arithmetic_result |
100 | Basic arithmetic result queries |
arithmetic_convoluted |
225 | Multi-step arithmetic with surface variation |
large_arithmetic_result |
100 | Large-number result queries |
multiplication_order |
300 | Multiplication with operand-order variants |
multiplication_result |
100 | Multiplication result queries |
subtraction_equivalence |
225 | Subtraction phrasing equivalences |
comparison_symmetric |
300 | Symmetric comparison statements |
comparison_convoluted |
225 | Complex comparison restatements |
unit_equivalence |
100 | Unit-conversion equivalences |
double_negation |
200 | Double-negation elimination |
negation_arithmetic |
150 | Negation embedded in arithmetic |
negation_depth |
100 | Nested negation (mixed depths) |
negation_even |
100 | Nested negation — even depths only |
negation_odd |
100 | Nested negation — odd depths only |
negation_depth_0 |
840 | Nested negation — depth 0 |
negation_depth_1 |
840 | Nested negation — depth 1 |
negation_depth_2 |
840 | Nested negation — depth 2 |
negation_depth_3 |
840 | Nested negation — depth 3 |
negation_depth_4 |
840 | Nested negation — depth 4 |
negation_depth_5 |
840 | Nested negation — depth 5 |
negation_depth_6 |
840 | Nested negation — depth 6 |
contrapositive |
200 | Contrapositive equivalences |
de_morgan |
200 | De Morgan's law restatements |
quantifier_scope |
92 | Quantifier-scope variants |
Fine-tuning experiments
The negation_depth_0–negation_depth_6 categories (840 groups each) are used in the depth-anchoring fine-tuning experiments described in the accompanying paper. For each training condition (e.g., depths 0–2), models are fine-tuned on the groups from those depths, then evaluated on all seven depths.
Versioning and maintenance
The current release is version 1.0.0. Future versions are planned to extend coverage with additional categories and more fine-grained consistency tests. Updates will be published on this repository following semantic versioning. The dataset is fully reproducible from the accompanying generation script (scripts/generate_ss_groups.py, seed 42).
Usage
import json
with open("ss_groups.json") as f:
groups = json.load(f)
# Filter to a single category
capital_groups = [g for g in groups if g["category"] == "capital_word_order"]
# Inspect phrasings and expected answer
for group in capital_groups[:3]:
print(group["expected"], group["questions"])
License
Citation
@inproceedings{krenc2026consistency,
title = {Consistency Is Not Correctness: Measuring Surface-Form Sensitivity and Misgeneralization in Language Models},
author = {Krenc, Krzysztof},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS) -- Evaluations and Datasets Track},
year = {2026},
url = {https://huggingface.co/datasets/Hravan/semantic-sensitivity},
}