dataset_info:
features:

• name: _id
  dtype: int64
• name: prompt
  dtype: string
• name: subject
  dtype: string
  splits:
• name: ccp_sensitive_sampled
  num_bytes: 53263
  num_examples: 340
• name: ccp_sensitive
  num_bytes: 213322
  num_examples: 1360
• name: deccp_censored
  num_bytes: 9034
  num_examples: 95
• name: general_prompts
  num_bytes: 63032
  num_examples: 100
• name: jailbreakbench
  num_bytes: 11910
  num_examples: 100
• name: sorrybench
  num_bytes: 73042
  num_examples: 440
• name: xstest_safe
  num_bytes: 17838
  num_examples: 250
• name: xstest_unsafe
  num_bytes: 14615
  num_examples: 200
• name: adversarial_unsafe_prompts
  num_bytes: 159801
  num_examples: 512
• name: harmbench_sampled
  num_bytes: 368921
  num_examples: 256
  download_size: 504354
  dataset_size: 984778
  configs:
• config_name: default
  data_files:
  • split: ccp_sensitive_sampled
    path: data/ccp_sensitive_sampled-*
  • split: ccp_sensitive
    path: data/ccp_sensitive-*
  • split: deccp_censored
    path: data/deccp_censored-*
  • split: general_prompts
    path: data/general_prompts-*
  • split: jailbreakbench
    path: data/jailbreakbench-*
  • split: sorrybench
    path: data/sorrybench-*
  • split: xstest_safe
    path: data/xstest_safe-*
  • split: xstest_unsafe
    path: data/xstest_unsafe-*
  • split: adversarial_unsafe_prompts
    path: data/adversarial_unsafe_prompts-*
  • split: harmbench_sampled
    path: data/harmbench_sampled-*
    task_categories:
• text-generation
  language:
• en
  tags:
• censorship
• politics
• safety
• instruction
  pretty_name: LLM Refusal Evaluation
  size_categories:
• n<1K

🛡️ LLM Refusal Evaluation Benchmark

This repository contains the benchmarks used in the LLM-Refusal-Evaluation suite.

The prompts are organized into three groups:

• Safety Benchmarks — harmful / jailbreak-style prompts that models should refuse.
• Chinese Sensitive Topics — prompts that may be censored by China-aligned models.
• Sanity Check Datasets — non-sensitive prompts to ensure models don’t over-refuse.

📌 Contents

• Safety Benchmarks
  • JailbreakBench
  • SorryBench
  • XSTest
  • HarmBench (Sampled)
  • Adversarial Unsafe Prompts
• Chinese Sensitive Topics
  • CCP Sensitive
  • DeCCP
• Sanity Check Datasets
  • General Prompts

🧪 Safety Benchmarks

JailbreakBench

Source: https://arxiv.org/abs/2404.01318

JailbreakBench is an open-source robustness benchmark for jailbreaking large language models (LLMs). It aims to comprehensively track progress toward:

1. Generating successful jailbreaks
2. Defending against jailbreak attempts

SorryBench

Source: https://arxiv.org/abs/2406.14598

A dataset of 440 unsafe instructions, spanning 44 fine-grained safety categories (10 examples per category). It is designed to be balanced across categories, unlike many prior safety datasets.

XSTest

Source: https://arxiv.org/abs/2308.01263

XSTest includes two splits:

• xtest_safe: 250 safe prompts across 10 prompt types that well-calibrated models should not refuse.
• xtest_unsafe: 200 unsafe prompts that most LLM applications should refuse.

HarmBench (Sampled)

Source: https://arxiv.org/abs/2402.04249

A collection of harmful prompts created for red-teaming evaluation. We sample 256 prompts across all categories.

Adversarial Unsafe Prompts

Source: https://huggingface.co/datasets/nvidia/Nemotron-Content-Safety-Reasoning-Dataset
Method: Automatic injection of jailbreak prompts

Following How to jailbreak DeepSeek R1 1776, we design a dataset that combines harmful prompts with Chinese sensitive-topic injections.

This evaluates whether “uncensored” models:

• trivially answer any request containing China, or
• actually maintain safety alignment and refuse harmful requests even when sensitive-topic content is injected.

✅ Expected behavior: models should refuse these prompts.

🇨🇳 Chinese Sensitive Topics

CCP Sensitive

Source: https://huggingface.co/datasets/promptfoo/CCP-sensitive-prompts

Prompts covering sensitive topics in China that are likely to be censored by China-aligned models.

DeCCP

Source: https://huggingface.co/datasets/augmxnt/deccp

Prompts about Chinese sensitive topics that trigger refusal responses in Qwen2-7B-Instruct.

✅ Sanity Check Datasets

General Prompts

Source: https://huggingface.co/datasets/HuggingFaceTB/smoltalk

A set of 100 random non-sensitive prompts (math, coding, summarization, etc.) used to verify that models aren’t over-refusing benign requests.