datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
XSTest
XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models
Paper: XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models
Data: xstest_prompts_v2
About
Without proper safeguards, large language models will follow malicious instructions and generate toxic content. This motivates safety efforts such as red-teaming and large-scale feedback learning, which aim to make models both helpful and harmless.… See the full description on the dataset page: https://huggingface.co/datasets/walledai/XSTest.XSTest
XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models
XSTest is a test suite designed to identify exaggerated safety / false refusal in Large Language Models (LLMs).
It comprises 250 safe prompts across 10 different prompt types, along with 200 unsafe prompts as contrasts.
The test suite aims to evaluate how well LLMs balance being helpful with being harmless by testing if they unnecessarily refuse to answer safe prompts that superficially… See the full description on the dataset page: https://huggingface.co/datasets/Paul/XSTest.xstest-v2-copy
XSTest Dataset for Testing Exaggerated Safety
Note, this is an upload of the data found here for easier research use.
All credit to the authors of the paper
The test prompts are subject to Creative Commons Attribution 4.0 International license. The model completions are subject to the original licenses specified by Meta, Mistral and OpenAI.
Loading the dataset
Use the following:
from datasets import load_dataset
dataset = load_dataset("natolambert/xstest-v2-copy)… See the full description on the dataset page: https://huggingface.co/datasets/natolambert/xstest-v2-copy.xstest-response
Dataset Card for XSTest-Response
Disclaimer:
The data includes examples that might be disturbing, harmful or upsetting. It includes a range of harmful topics such as discriminatory language and discussions
about abuse, violence, self-harm, sexual content, misinformation among other high-risk categories. The main goal of this data is for advancing research in building safe LLMs.
It is recommended not to train a LLM exclusively on the harmful examples.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/allenai/xstest-response.XSTestXSTestXSTest-ITXSTest_koxstest-overrefusal
XSTest — Over-Refusal Subset
A filtered subset of XSTest (Röttger et al. 2024,
arXiv:2308.01263) intended for measuring over-refusal only.
The upstream XSTest test split contains 250 prompts labeled safe — prompts that look harmful but
are intended to be benign. Manual review found that 36 of the 250 "safe" prompts are actually
borderline or unsafe: refusing them is defensible, so they shouldn't count toward an over-refusal
metric. This subset keeps only the 214 prompts where… See the full description on the dataset page: https://huggingface.co/datasets/jkminder/xstest-overrefusal.xstest_ptpt
XSTest-PT
Portuguese machine translation of XSTest, a benchmark for identifying exaggerated safety behaviors in language models.
Translated using a Finetuned GemmaX2-9B for pt-PT.
Original Dataset: https://huggingface.co/datasets/Paul/XSTest
Note: This dataset is machine translated and may contain translation errors or artifacts.
This dataset is provided as part of the AMALIA project and is included in AMALIA-Bench, a comprehensive benchmark suite for evaluating large… See the full description on the dataset page: https://huggingface.co/datasets/amalia-llm/xstest_ptpt.XSTestid-0001-reverse-training-unlearn-xstestxstest-response-koevaluation_xstest_unsafe_safeXSTestevaluation_xstest_safeid-0009-reverse-training-unlearn-xstestXSTest
XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models
XSTest is a test suite designed to identify exaggerated safety / false refusal in Large Language Models (LLMs).
It comprises 250 safe prompts across 10 different prompt types, along with 200 unsafe prompts as contrasts.
The test suite aims to evaluate how well LLMs balance being helpful with being harmless by testing if they unnecessarily refuse to answer safe prompts that superficially… See the full description on the dataset page: https://huggingface.co/datasets/kevin-giskard/XSTest.XSTest-R1evaluation_xstest_unsafexstest-llama3.1-8b-inst-completionsAPTO-XSTest-JA
APTO-XSTest-JA
APTO-XSTest-JA is a Japanese translated and annotated version of the XSTest dataset for AI safety evaluation research.
XSTest is a test suite designed to identify exaggerated safety behaviours in large language models, including cases where models refuse clearly safe prompts because they contain sensitive wording or resemble unsafe requests.
This dataset includes:
Japanese translations of XSTest prompts
Japanese refusal / non-refusal reference responses
Refusal… See the full description on the dataset page: https://huggingface.co/datasets/APTO-001/APTO-XSTest-JA.evaluation_xstest_unsafe_unsafeid-0002-xstest-completionsXSTest-In-Character-Refusals
🎭 In-Character Safety & Alignment Dataset (XSTest-Based)
Dataset Summary
This dataset is designed to train Large Language Models to maintain strict persona adherence during roleplay, even when responding to tricky, unsafe, or out-of-domain prompts.
A common issue with standard safety tuning is that models often abandon their assigned persona and revert to generic AI safety responses (e.g., "As an AI language model, I cannot..."). This dataset addresses that… See the full description on the dataset page: https://huggingface.co/datasets/mahdieh-sjp/XSTest-In-Character-Refusals.XSTest-ptxstest-v2-copy
XSTest Dataset for Testing Exaggerated Safety
Note, this is an upload of the data found here for easier research use.
All credit to the authors of the paper
The test prompts are subject to Creative Commons Attribution 4.0 International license. The model completions are subject to the original licenses specified by Meta, Mistral and OpenAI.
Loading the dataset
Use the following:
from datasets import load_dataset
dataset = load_dataset("natolambert/xstest-v2-copy)… See the full description on the dataset page: https://huggingface.co/datasets/boolishs/xstest-v2-copy.xstest-llama3.1-8b-inst-safe-rlhf-0710-completionsid-0009-xstestid-0010-xstest