Omkar2118/datasets huggingface dataset:
wikieval

WikiEval
Dataset for to do correlation analysis of difference metrics proposed in Ragas This dataset was generated from 50 pages from Wikipedia with edits post 2022.

Column description
question: a question that can be answered from the given Wikipedia page (source).
source: The source Wikipedia page from which the question and context are generated.
grounded_answer: answer grounded on context_v1
ungrounded_answer: answer generated without context_v1
poor_answer: answer with poor relevancy compared to grounded_answer and ungrounded_answer
context_v1: Ideal context to answer the given question
contetx_v2: context that contains redundant information compared to context_v1
BoLD
Full name: Body Language Dataset

Yu Luo, Jianbo Ye, Reginald B. Adams, Jr., Jia Li, Michelle G. Newman and James Z. Wang, ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild,'' International Journal of Computer Vision, vol. 128, no. 1, pp. 1-25, 2020.

Introducing paper: ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild
AART
Full name: AI-Assisted Red-Teaming

AART serves as an automated alternative to the current manual red-teaming efforts.
The primary goal is to evaluate the safety of LLM generations in various application contexts.

Adversarial Testing of LLMs:
Adversarial testing is crucial for ensuring the safe and responsible deployment of LLMs.
The authors introduce a novel approach to generate adversarial evaluation datasets.
These datasets are used to assess the safety of LLM outputs in real-world scenarios.

Key Features of AART:
Diverse Data Generation: AART generates evaluation datasets with high diversity of content characteristics. This includes concepts that are sensitive, harmful, and specific to various cultural and geographic regions and application scenarios.

AI-Assisted Recipes: The data generation process is steered by AI-assisted recipes. These recipes define, scope, and prioritize diversity within the application context.
Structured LLM-Generation Process: AART feeds the diverse data into a structured LLM-generation process. This helps scale up evaluation priorities.
Promising Results: Compared to some state-of-the-art tools, AART demonstrates promising results in terms of concept coverage and data quality.

Introducing paper: AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications
RedEval

**RedEval** is a safety evaluation benchmark designed to assess the robustness of large language models (LLMs) against harmful prompts. It simulates and evaluates LLM applications across various scenarios, all while **eliminating the need for human intervention**. Here are the key aspects of RedEval:

1. **Purpose**: RedEval aims to evaluate LLM safety using a technique called **Chain of Utterances (CoU)**-based prompts. CoU prompts are effective at breaking the safety guardrails of various LLMs, including **GPT-4**, **ChatGPT**, and open-source models.

2. **Safety Assessment**: RedEval provides **simple scripts** to evaluate both **closed-source systems** (such as ChatGPT and GPT-4) and **open-source LLMs** on its benchmark. The evaluation focuses on **harmful questions** and computes the **Attack Success Rate (ASR)**.

3. **Question Banks**:
    - **HarmfulQA**: Consists of **1,960 harmful questions** covering **10 topics** and approximately **10 subtopics** each.
    - **DangerousQA**: Contains **200 harmful questions** across **6 adjectives**: racist, stereotypical, sexist, illegal, toxic, and harmful.
    - **CategoricalQA**: Includes **11 categories of harm**, each with **5 sub-categories**, available in English, Chinese, and Vietnamese.
    - **AdversarialQA**: Provides a set of **500 instructions** to tease out harmful behaviors from the model.

4. **Safety Alignment**: RedEval also offers code to perform **safety alignment** of LLMs. For instance, it aligns **Vicuna-7B** on **HarmfulQA**, resulting in a safer version of Vicuna that is more robust against RedEval.

5. **Installation**:
    - Create a conda environment: `conda create --name redeval -c conda-forge python=3.11`
    - Activate the environment: `conda activate redeval`
    - Install required packages: `pip install -r requirements.txt`
    - Store API keys in the `api_keys` directory for use by the LLM as a judge and the `generate_responses.py` script for closed-source models.

6. **Prompt Templates**:
    - Choose a prompt template for red-teaming:
        - **Chain of Utterances (CoU)**: Effective at breaking safety guardrails.
        - **Chain of Thoughts (CoT)**
        - **Standard prompt**
        - **Suffix prompt**
        - Note: Different LLMs may require slight variations in the prompt template.

7. **How to Perform Red-Teaming**:
    - **Step 0**: Decide on the prompt template.
    - **Step 1**: Generate model outputs on harmful questions by providing a path to the question bank and the red-teaming prompt.

Introducing paper: Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment
Redteaming Resistance Benchmark

The **Redteaming Resistance Benchmark** is a project aimed at evaluating the robustness of language models, both open-source and black-box, through **redteaming attacks**. These attacks involve systematically challenging and testing models with carefully crafted prompts to uncover their failure modes and vulnerabilities. In other words, it reveals where these models are susceptible to generating problematic outputs¹².

Here are some key points about the **Redteaming Resistance Benchmark**:

- **Purpose**: The project aims to identify weaknesses in language models by simulating adversarial scenarios.
- **Datasets Used**: The benchmark uses several datasets to evaluate model performance, including:
    - **Advbench**: A dataset of adversarial behaviors ranging from profanity, discrimination, to violence.
    - **AART**: A collection of generated adversarial behaviors with diverse cultural, geographic, and application settings.
    - **Beavertails**: A dataset for safety alignment research in large language models.
    - **Do Not Answer**: A dataset of prompts to which responsible language models do not answer.
    - **RedEval - HarmfulQA** and **RedEval - DangerousQA**: Datasets of harmful questions covering various topics.
    - **Student-Teacher Prompting**: A dataset of harmful prompts that successfully broke a specific language model.
    - **SAP**: Attacks generated through in-context learning to mimic human speech.
- **Content Categories**: The benchmark evaluates model performance across 15 different categories, including illegal activity, harm, hate, discrimination, and more¹.

(1) GitHub - haizelabs/redteaming-resistance-benchmark. https://github.com/haizelabs/redteaming-resistance-benchmark.
(2) Introducing the Red-Teaming Resistance Leaderboard - Hugging Face. https://huggingface.co/blog/leaderboard-haizelab.
(3) Redteaming Resistance Leaderboard - a Hugging Face Space by Nymbo. https://huggingface.co/spaces/Nymbo/red-teaming-resistance-benchmark.
(4) Redteaming Resistance Benchmark - GitHub. https://github.com/jussker/redteaming-resistance-benchmark/blob/main/README.md.
Student-Teacher Prompting

**Student-Teacher Prompting** is an instructional strategy used to guide a learner's behavior. It is particularly helpful for teaching new skills or encouraging desired behaviors. Here are some examples of different types of prompts that teachers or educators might use:

1. **Verbal Prompts**:
    - **Verbal Instructions**: These involve giving clear, step-by-step verbal directions to guide the student through a task. For instance, when teaching someone to make a sandwich, the instructor might say, "First, take two slices of bread and place them on a plate."
    - **Verbal Cues**: These are less structured than instructions and provide just-in-time support. For example, when coaching someone on a dance move, the coach might say, "Don't forget to step to the right before you turn."
    - **Verbal Praise**: Praising someone's efforts reinforces desired behaviors. For instance, if a child puts away their toys, a parent might say, "Great job! You put your toys away all by yourself! Let's go get some ice cream!"

2. **Nonverbal Prompts**:
    - These involve gestures or body language:
        - **Modeling**: Demonstrating the desired behavior for the learner. For instance, having a model picture as a stimulus when painting your own picture.
        - **Visual Prompts**: Using visual cues such as pointing to a photograph or video to guide the student.

3. **Physical Prompts**:
    - These involve gentle physical contact:
        - **Partial Physical Prompt**: The teacher adjusts or guides the student's behavior in the right direction using their hands.
        - **Full Physical Prompt**: The teacher completely controls the student's movements through the entire sequence of the target behavior.

**Prompting** is often used in **Applied Behavior Analysis (ABA)** to assist children with learning difficulties in acquiring target behaviors. It falls along a continuum from least intrusive to most intrusive, allowing educators to tailor their approach to individual needs¹²³.

(1) 18 Prompting Examples (2024) - Helpful Professor. https://helpfulprofessor.com/prompting-examples/.
(2) Prompting - Instructional Strategies (PENT) - California. https://pent.ca.gov/is/is/prompting.aspx.
(3) Everything You Need To Know About Prompting Teaching Strategy. https://numberdyslexia.com/prompting-teaching-strategy/.
SAP

The **SAP benchmark** is a significant development in the realm of **attack prompt generation** for **red teaming** and **defending large language models (LLMs)**. Let's delve into the details:

1. **Objective**:
   - The primary goal of the SAP benchmark is to evaluate the safety and robustness of LLMs against **red teaming attacks**.
   - Red teaming attacks involve inducing LLMs to generate harmful or inappropriate content.

2. **Methodology**:
   - The SAP benchmark combines both manual and automatic methods to generate high-quality attack prompts.
   - It leverages the impressive capabilities of newly emerged LLMs.
   - Specifically, it instructs LLMs to mimic human-generated prompts through **in-context learning**.
   - The attack framework is designed to create these prompts.

3. **Defense Framework**:
   - In addition to attacking LLMs, the SAP benchmark proposes a defense framework.
   - This framework fine-tunes victim LLMs through **iterative interactions** with the attack framework.
   - The goal is to enhance the safety of LLMs against red teaming attacks.

4. **Validation and Datasets**:
   - Extensive experiments on different LLMs validate the effectiveness of both the attack and defense frameworks.
   - As part of this work, the authors release a series of **attack prompt datasets** named **SAP** with varying sizes.
   - These datasets facilitate safety evaluation and enhancement for a broader range of LLMs¹.

Introducing paper: Attack Prompt Generation for Red Teaming and Defending Large Language Models
CAESAR-Radi
Full name: CAESAR-Radi: SAR-Ship-Dataset

This dataset labeled by SAR experts was created using 102 Chinese Gaofen-3 images and 108 Sentinel-1 images. It consists of 39,729 ship chips(remove some repeat clips) of 256 pixels in both range and azimuth. These ships mainly have distinct scales and backgrounds. It can be used to develop object detectors for multi-scale and small object detection. The details of this dataset is referred to "Wang, Yuanyuan, Chao Wang, Hong Zhang, Yingbo Dong, and Sisi Wei. 2019. "A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds." Remote Sensing 11 (7). doi: 10.3390/rs11070765."

Currently, we have released all the dataset for ship detection using SAR images, which has 39,729 ship chips.

If you feel this dataset is useful, please cite as the following format.

@Article{rs11070765, AUTHOR = {Wang, Yuanyuan and Wang, Chao and Zhang, Hong and Dong, Yingbo and Wei, Sisi},
TITLE = {A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds},
JOURNAL = {Remote Sensing},
VOLUME = {11},
YEAR = {2019},
NUMBER = {7},
ARTICLE-NUMBER = {765},
URL = {http://www.mdpi.com/2072-4292/11/7/765},
ISSN = {2072-4292},
DOI = {10.3390/rs11070765}}

Besides, we own many thanks to China Center for Resources Satellite Data and Application and Copernicus Open Access Hub for providing Gaofen-3 images and Sentinel-1 images, respectively.

This research was funded by the National Natural Science Foundation of China under Grant 41331176 and National Key Research and Development Program of China (2016YFB0501501).