ChatGPT was the chatbot that kickstarted the generative AI revolution, which has been responsible for hundreds of billions of dollars in data centres, graphics chips and AI startups. Launched by...

All GPT-4 Generated Datasets

Every chat dataset generated by GPT-4 from Huggingface at the same format

From [Huggingface datasets]

About this dataset

How to use the dataset

The dataset includes all chat conversations generated by GPT-4 that are hosted on open Huggingface datasets. Everything is converted to the same format so the datasets can be easily merged and used for large scale training of LLMs.

Acknowledgements

This dataset is a collection of several single chat datasets. If you use this dataset in your research, please credit the original authors of the internal datasets. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

In the week from October 19 to 25, 2025, global Google searches for the word "ChatGPT" reached a peak of 100 index points, indicating a significant increase in interest and thus the highest interest over the observed period. On October 21, 2025, OpenAI introduced ChatGPT Atlas, a web browser with ChatGPT built in. Interest in the chatbot, developed by U.S.-based OpenAI and launched in November 2022, started rising in the week ending December 3, 2022. ChatGPT, which stands for Chat Generative Pre-trained Transformer, is an AI-powered auto-generative text system able to give human-sounding replies and reproduce human-like interactions when prompted.

Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.

This study aimed to evaluate the performance of Chat Generative Pre-Trained Transformer (ChatGPT) with respect to standardized urology multiple-choice items in the United States. In total, 700 multiple-choice urology board exam-style items were submitted to GPT-3.5 and GPT-4, and responses were recorded. Items were categorized based on topic and question complexity (recall, interpretation, and problem-solving). The accuracy of GPT-3.5 and GPT-4 was compared across item types in February 2024.

Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants

Overview

This dataset can be used to train Large Language Models such as GPT, Llama2 and Falcon, both for Fine Tuning and Domain Adaptation.

The dataset has the following specs:

• Use Case: Intent Detection
• Vertical: Customer Service
• 27 intents assigned to 10 categories
• 26872 question/answer pairs, around 1000 per intent
• 30 entity/slot types
• 12 different types of language generation tags

The categories and intents have been selected from Bitext's collection of 20 vertical-specific datasets, covering the intents that are common across all 20 verticals. The verticals are:

• Automotive, Retail Banking, Education, Events & Ticketing, Field Services, Healthcare, Hospitality, Insurance, Legal Services, Manufacturing, Media Streaming, Mortgages & Loans, Moving & Storage, Real Estate/Construction, Restaurant & Bar Chains, Retail/E-commerce, Telecommunications, Travel, Utilities, Wealth Management

For a full list of verticals and its intents see https://www.bitext.com/chatbot-verticals/.

The question/answer pairs have been generated using a hybrid methodology that uses natural texts as source text, NLP technology to extract seeds from these texts, and NLG technology to expand the seed texts. All steps in the process are curated by computational linguists.

Dataset Token Count

The dataset contains an extensive amount of text data across its 'instruction' and 'response' columns. After processing and tokenizing the dataset, we've identified a total of 3.57 million tokens. This rich set of tokens is essential for training advanced LLMs for AI Conversational, AI Generative, and Question and Answering (Q&A) models.

Fields of the Dataset

Each entry in the dataset contains the following fields:

• flags: tags (explained below in the Language Generation Tags section)
• instruction: a user request from the Customer Service domain
• category: the high-level semantic category for the intent
• intent: the intent corresponding to the user instruction
• response: an example expected response from the virtual assistant

Categories and Intents

The categories and intents covered by the dataset are:

• ACCOUNT: create_account, delete_account, edit_account, recover_password, registration_problems, switch_account
• CANCELLATION_FEE: check_cancellation_fee
• CONTACT: contact_customer_service, contact_human_agent
• DELIVERY: delivery_options, delivery_period
• FEEDBACK: complaint, review
• INVOICE: check_invoice, get_invoice
• ORDER: cancel_order, change_order, place_order, track_order
• PAYMENT: check_payment_methods, payment_issue
• REFUND: check_refund_policy, get_refund, track_refund
• SHIPPING_ADDRESS: change_shipping_address, set_up_shipping_address
• SUBSCRIPTION: newsletter_subscription

Entities

The entities covered by the dataset are:

• {{Order Number}}, typically present in:
• Intents: cancel_order, change_order, change_shipping_address, check_invoice, check_refund_policy, complaint, delivery_options, delivery_period, get_invoice, get_refund, place_order, track_order, track_refund
• {{Invoice Number}}, typically present in:
  • Intents: check_invoice, get_invoice
• {{Online Order Interaction}}, typically present in:
  • Intents: cancel_order, change_order, check_refund_policy, delivery_period, get_refund, review, track_order, track_refund
• {{Online Payment Interaction}}, typically present in:
  • Intents: cancel_order, check_payment_methods
• {{Online Navigation Step}}, typically present in:
  • Intents: complaint, delivery_options
• {{Online Customer Support Channel}}, typically present in:
  • Intents: check_refund_policy, complaint, contact_human_agent, delete_account, delivery_options, edit_account, get_refund, payment_issue, registration_problems, switch_account
• {{Profile}}, typically present in:
  • Intent: switch_account
• {{Profile Type}}, typically present in:
  • Intent: switch_account
• {{Settings}}, typically present in:
  • Intents: cancel_order, change_order, change_shipping_address, check_cancellation_fee, check_invoice, check_payment_methods, contact_human_agent, delete_account, delivery_options, edit_account, get_invoice, newsletter_subscription, payment_issue, place_order, recover_password, registration_problems, set_up_shipping_address, switch_account, track_order, track_refund
• {{Online Company Portal Info}}, typically present in:
  • Intents: cancel_order, edit_account
• {{Date}}, typically present in:
  • Intents: check_invoice, check_refund_policy, get_refund, track_order, track_refund
• {{Date Range}}, typically present in:
  • Intents: check_cancellation_fee, check_invoice, get_invoice
• {{Shipping Cut-off Time}}, typically present in:
  • Intent: delivery_options
• {{Delivery City}}, typically present in:
  • Inten...

We have compiled a dataset that consists of textual articles including common terminology, concepts and definitions in the field of computer science, artificial intelligence, and cyber security. This dataset consists of both human-generated text and OpenAI’s ChatGPT-generated text. Human-generated answers were collected from different computer science dictionaries and encyclopedias including “The Encyclopedia of Computer Science and Technology” and "Encyclopedia of Human-Computer Interaction". AI-generated content in our dataset was produced by simply posting questions to OpenAI’s ChatGPT and manually documenting the resulting responses. A rigorous data-cleaning process has been performed to remove unwanted Unicode characters, styling and formatting tags. To structure our dataset for binary classification, we combined both AI-generated and Human-generated answers into a single column and assigned appropriate labels to each data point (Human-generated = 0 and AI-generated = 1).

This creates our article-level dataset (article_level_data.csv) which consists of a total of 1018 articles, 509 AI-generated and 509 Human-generated. Additionally, we have divided each article into its sentences and labelled them accordingly. This is mainly to evaluate the performance of classification models and pipelines when it comes to shorter sentence-level data points. This constructs our sentence-level dataset (sentence_level_data.csv) which consists of a total of 7344 entries (4008 AI-generated and 3336 Human-generated).

We appreciate it, if you cite the following article if you happen to use this dataset in any scientific publication:

Maktab Dar Oghaz, M., Dhame, K., Singaram, G., & Babu Saheer, L. (2023). Detection and Classification of ChatGPT Generated Contents Using Deep Transformer Models. Frontiers in Artificial Intelligence.

https://www.techrxiv.org/users/692552/articles/682641/master/file/data/ChatGPT_generated_Content_Detection/ChatGPT_generated_Content_Detection.pdf

Comparison of Seconds to Output 500 Tokens, including reasoning model 'thinking' time; Lower is better by Model

Comprehensive comparison of Latency (Time to First Token) vs. Output Speed (Output Tokens per Second) by Model

Comparison of Seconds to First Token Received; Lower is better by Model

The rivalry between ChatGPT and Google Gemini defines the generative AI landscape. ChatGPT remains the leader in active engagement, while Gemini closes the gap through mass distribution. From corporate reports to web traffic studies, figures speak clearly about adoption, reach, and momentum. Explore what makes each platform stand out, and what...

Comparison of Seconds to First Answer Token Received; Accounts for Reasoning Model 'Thinking' time by Model

Purpose and Scope

The AstroChat dataset is a collection of 901 dialogues, synthetically generated, tailored to the specific domain of Astronautics / Space Mission Engineering. This dataset will be frequently updated following feedback from the community. If you would like to contribute, please reach out in the community discussion.

Intended Use

The dataset is intended to be used for supervised fine-tuning of chat LLMs (Large Language Models). Due to its currently limited size, you should use a pre-trained instruct model and ideally augment the AstroChat dataset with other datasets in the area of (Science Technology, Engineering and Math).

Quickstart

To be completed

DATASET DESCRIPTION

Access

• Manual download from Hugging face hub: https://huggingface.co/datasets/patrickfleith/AstroChat
• Or with python: python from datasets import load_dataset dataset = load_dataset("patrickfleith/AstroChat")

Structure

901 generated conversations between a simulated user and AI-assistant (more on the generation method below). Each instance is made of the following field (column): - id: a unique identifier to refer to this specific conversation. Useeful for traceability purposes, especially for further processing task or merge with other datasets. - topic: a topic within the domain of Astronautics / Space Mission Engineering. This field is useful to filter the dataset by topic, or to create a topic-based split. - subtopic: a subtopic of the topic. For instance in the topic of Propulsion, there are subtopics like Injector Design, Combustion Instability, Electric Propulsion, Chemical Propulsion, etc. - persona: description of the persona used to simulate a user - opening_question: the first question asked by the user to start a conversation with the AI-assistant - messages: the whole conversation messages between the user and the AI assistant in already nicely formatted for rapid use with the transformers library. A list of messages where each message is a dictionary with the following fields: - role: the role of the speaker, either user or assistant - content: the message content. For the assistant, it is the answer to the user's question. For the user, it is the question asked to the assistant.

Important See the full list of topics and subtopics covered below.

Metadata

Dataset is version controlled and commits history is available here: https://huggingface.co/datasets/patrickfleith/AstroChat/commits/main

Generation Method

We used a method inspired from Ultrachat dataset. Especially, we implemented our own version of Human-Model interaction from Sector I: Questions about the World of their paper:

Ding, N., Chen, Y., Xu, B., Qin, Y., Zheng, Z., Hu, S., ... & Zhou, B. (2023). Enhancing chat language models by scaling high-quality instructional conversations. arXiv preprint arXiv:2305.14233.

Step-by-step description

• Defined a set of user persona
• Defined a set of topics/ disciplines within the domain of Astronautics / Space Mission Engineering
• For each topics, we defined a set of subtopics to narrow down the conversation to more specific and niche conversations (see below the full list)
• For each subtopic we generate a set of opening questions that the user could ask to start a conversation (see below the full list)
• We then distil the knowledge of an strong Chat Model (in our case ChatGPT through then api with gpt-4-turbo model) to generate the answers to the opening questions
• We simulate follow-up questions from the user to the assistant, and the assistant's answers to these questions which builds up the messages.

Future work and contributions appreciated

• Distil knowledge from more models (Anthropic, Mixtral, GPT-4o, etc...)
• Implement more creativity in the opening questions and follow-up questions
• Filter-out questions and conversations which are too similar
• Ask topic and subtopic expert to validate the generated conversations to have a sense on how reliable is the overall dataset

Languages

All instances in the dataset are in english

Size

901 synthetically-generated dialogue

USAGE AND GUIDELINES

License

AstroChat © 2024 by Patrick Fleith is licensed under Creative Commons Attribution 4.0 International

Restrictions

No restriction. Please provide the correct attribution following the license terms.

Citation

Patrick Fleith. (2024). AstroChat - A Dataset of synthetically generated conversations for LLM supervised fine-tuning in the domain of Space Mission Engineering and Astronautics (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11531579

Update Frequency

Will be updated based on feedbacks. I am also looking for contributors. Help me create more datasets for Space Engineering LLMs :)

Have a feedback or spot an error?

Use the ...

Comparison of Output Tokens per Second; Higher is better by Model

Amazon Question-Answer Dataset - Extracted and Preprocessed for GPT-2 Fine-Tuning

Description:

This dataset comprises raw data meticulously extracted from the Amazon Question-Answer dataset. Its primary focus is to extract question and answer pairs and convert them into a structured JSON format.

The extracted dataset has undergone thorough preprocessing to make it suitable for fine-tuning the GPT-2 language model. This refined dataset, known as "pre-processed-chat-dataset," is readily available for exploration on Kaggle and can also be accessed through my Kaggle account with the username "ben alla ismail."

Key Dataset Highlights:

• Data Source: Amazon Question-Answer Dataset
• Format: JSON (Structured Question-Answer Pairs)
• Purpose: Fine-Tuning the GPT-2 Model

Dataset Insights:

Here is a glimpse of the dataset structure: - Sample Entry: json { "noitseuq": "Does this panel come with the connection ribbon cable?", "rewsna": "No, it doesn't. I used the old one." }

Data Statistics: - Training Set: 2.17 GB - Test Set: 273.34 MB - Validation Set: 271.41 MB

For deeper insights, regular updates, and easy access to this valuable dataset, please explore the following links:

• LinkedIn: Ismail Ben Alla
• GitHub: benisalla
• Instagram: ismail_ben_alla
• Twitter: ismail_ben_alla

Comprehensive comparison of Artificial Analysis Intelligence Index vs. Seconds to Output 500 Tokens, including reasoning model 'thinking' time by Model

Comprehensive comparison of Artificial Analysis Intelligence Index vs. Output Speed (Output Tokens per Second) by Model

Title: Preprocessed Dataset for Chatbot Model-Based Translation Transformer

Description:

This meticulously preprocessed dataset originates from raw chat data, accessible through the provided link. It has been transformed into a binary format to optimize efficiency and accessibility for deep learning applications.

The dataset is meticulously structured in binary format, essentially representing streams of integers. This format is particularly well-suited for the streamlined management of large-scale datasets in deep learning tasks.

Key Dataset Components:

• Train.bin (5.85 GB): The training dataset.
• Test.bin (731.35 MB): The testing dataset.
• Val.bin (731.35 MB): The validation dataset.

To interact with this data, users can seamlessly utilize np.memmap from the NumPy library, offering effortless access and manipulation for various machine learning endeavors.

tably, the dataset employs the GPT-2 tokenizer with the inclusion of special tokens:

Comparison of Tokens used to run all evaluations in the Artificial Analysis Intelligence Index by Model

🧠 Awesome ChatGPT Prompts [CSV dataset]

This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub

  License

CC-0