Synthetic Data Generation for NLP Market Outlook

According to our latest research, the synthetic data generation for NLP market size reached USD 420 million globally in 2024, reflecting strong momentum driven by the rapid adoption of artificial intelligence across industries. The market is projected to expand at a robust CAGR of 32.4% from 2025 to 2033, reaching a forecasted value of USD 4.7 billion by 2033. This remarkable growth is primarily fueled by the increasing demand for high-quality, privacy-compliant data to train advanced natural language processing models, as well as the rising need to overcome data scarcity and bias in AI applications.

One of the most significant growth factors for the synthetic data generation for NLP market is the escalating requirement for large, diverse, and unbiased datasets to power next-generation NLP models. As organizations across sectors such as BFSI, healthcare, retail, and IT accelerate AI adoption, the limitations of real-world datasets—such as privacy risks, regulatory constraints, and inherent biases—become more pronounced. Synthetic data offers a compelling solution by generating realistic, high-utility language data without exposing sensitive information. This capability is particularly valuable in highly regulated industries, where compliance with data protection laws like GDPR and HIPAA is mandatory. As a result, enterprises are increasingly integrating synthetic data generation solutions into their NLP pipelines to enhance model accuracy, mitigate bias, and ensure robust data privacy.

Another key driver is the rapid technological advancements in generative AI and deep learning, which have significantly improved the quality and realism of synthetic language data. Recent breakthroughs in large language models (LLMs) and generative adversarial networks (GANs) have enabled the creation of synthetic text that closely mimics human language, making it suitable for a wide range of NLP applications including text classification, sentiment analysis, and machine translation. The growing availability of scalable, cloud-based synthetic data generation platforms further accelerates adoption, enabling organizations of all sizes to access cutting-edge tools without substantial upfront investment. This democratization of synthetic data technology is expected to propel market growth over the forecast period.

The proliferation of AI-driven automation and digital transformation initiatives across enterprises is also catalyzing the demand for synthetic data generation for NLP. As businesses seek to automate customer service, enhance content moderation, and personalize user experiences, the need for large-scale, high-quality NLP training data is surging. Synthetic data not only enables faster model development and deployment but also supports continuous learning and adaptation in dynamic environments. Moreover, the ability to generate rare or edge-case language data allows organizations to build more robust and resilient NLP systems, further driving market expansion.

From a regional perspective, North America currently dominates the synthetic data generation for NLP market, accounting for over 37% of global revenue in 2024. This leadership is attributed to the strong presence of leading AI technology vendors, early adoption of NLP solutions, and a favorable regulatory landscape that encourages innovation. Europe follows closely, driven by stringent data privacy regulations and significant investment in AI research. The Asia Pacific region is poised for the fastest growth, with a projected CAGR of 36% through 2033, fueled by rapid digitalization, expanding AI ecosystems, and increasing government support for AI initiatives. Other regions such as Latin America and the Middle East & Africa are also witnessing growing interest, albeit from a smaller base, as enterprises in these markets begin to recognize the value of synthetic data for NLP applications.

Component Analysis

The synthetic data generation for NLP market is s

Synthetic Data Generation for NLP Market Outlook

According to our latest research, the global synthetic data generation for NLP market size reached USD 620 million in 2024 and is expected to grow at a robust CAGR of 35.7% during the forecast period from 2025 to 2033. By 2033, the market is projected to hit approximately USD 7.52 billion. This exceptional growth is primarily driven by the escalating demand for high-quality and diverse datasets to train advanced natural language processing (NLP) models, coupled with increasing concerns regarding data privacy and the rising adoption of artificial intelligence across various industry verticals.

One of the most significant growth factors in the synthetic data generation for NLP market is the rapid advancement in AI and machine learning technologies. Organizations are increasingly leveraging synthetic data to overcome the limitations of real-world data, such as scarcity, high costs, and privacy concerns. Synthetic data enables the creation of large, labeled datasets tailored for specific NLP tasks, which accelerates model development and enhances accuracy. As NLP applications become more sophisticated and are integrated into critical business functions, the need for diverse, unbiased, and privacy-compliant data becomes even more pronounced. This trend is particularly evident in sectors like healthcare and finance, where sensitive information must be handled with utmost care, and synthetic data offers a viable solution to regulatory challenges.

Another driving force behind market expansion is the growing adoption of cloud-based solutions for synthetic data generation. Cloud platforms provide scalable infrastructure, enabling organizations to generate and utilize synthetic NLP datasets without heavy upfront investments in hardware. The cloud also facilitates collaboration across geographically dispersed teams, making it easier to develop, test, and deploy NLP models at scale. Furthermore, the integration of synthetic data generation tools with popular cloud-based AI development environments streamlines workflows, reduces time-to-market, and supports continuous model improvement. As businesses increasingly migrate their operations to the cloud, the demand for cloud-based synthetic data generation solutions is expected to surge.

The proliferation of NLP applications across diverse sectors is further fueling market growth. In industries such as retail, e-commerce, telecommunications, and media, NLP-driven solutions like chatbots, sentiment analysis, and personalized recommendations are becoming essential for enhancing customer experience and operational efficiency. Synthetic data generation enables these industries to rapidly iterate and optimize their NLP models, even in the absence of extensive real-world data. Moreover, the ability to simulate rare or edge-case scenarios with synthetic data allows organizations to build more robust and resilient NLP systems. As digital transformation initiatives accelerate worldwide, synthetic data generation for NLP will continue to be a cornerstone of innovation and competitive differentiation.

From a regional standpoint, North America currently dominates the synthetic data generation for NLP market due to its mature AI ecosystem, significant R&D investments, and the presence of leading technology companies. Europe follows closely, driven by stringent data protection regulations such as GDPR, which incentivize the adoption of privacy-preserving synthetic data solutions. Meanwhile, the Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, expanding IT infrastructure, and increasing government support for AI initiatives. Latin America and the Middle East & Africa are also showing promising growth trajectories as organizations in these regions recognize the value of synthetic data in accelerating NLP adoption and overcoming data-related challenges.

Component Analysis

The component segment of the synthetic data generation for NLP market is bifurcated into software and services. The software segment currently holds the largest market share, owing to the widespread adoption of advanced synthetic data generation platforms and tools. These software solutions offer a range of functionalities, including data augmentation, anonymization, and automated labeling, which are essential for training high-performance NLP models. The continuous evolution of these platforms, with the integration of sophisticated algorithms and user-friendly interfaces,

Synthetic Data for NLP Market Outlook

According to our latest research, the global Synthetic Data for NLP market size reached USD 635 million in 2024, with a robust growth trajectory underpinned by rising adoption across industries. The market is projected to expand at a CAGR of 34.7% during the forecast period, reaching an estimated USD 7.6 billion by 2033. This exceptional growth is primarily driven by the increasing need for high-quality, diverse, and privacy-compliant datasets for natural language processing (NLP) model training and testing, as organizations face mounting data privacy regulations and seek to accelerate AI innovation.

One of the most significant growth factors in the Synthetic Data for NLP market is the escalating demand for large-scale annotated datasets required to train advanced NLP models, such as those used in generative AI, conversational interfaces, and automated sentiment analysis. Traditional data collection methods are often hampered by privacy concerns, data scarcity, and the high costs of manual annotation. Synthetic data generation addresses these challenges by enabling the creation of vast, customizable datasets that mirror real-world linguistic complexity without exposing sensitive information. As organizations increasingly deploy NLP solutions in customer service, healthcare, finance, and beyond, the ability to generate synthetic text, audio, and multimodal data at scale is transforming the AI development lifecycle and reducing time-to-market for new applications.

Another key driver is the evolving regulatory landscape surrounding data privacy and security, particularly in regions such as Europe and North America. The introduction of stringent frameworks like GDPR and CCPA has limited the availability of real-world data for AI training, making synthetic data an attractive alternative for compliance-conscious enterprises. Unlike traditional anonymization techniques, synthetic data preserves statistical properties and semantic relationships, ensuring model performance without risking re-identification. This capability is especially valuable in sectors such as healthcare and banking, where data sensitivity is paramount. The growing recognition of synthetic data as a privacy-enhancing technology is fueling investments in research, platform development, and cross-industry collaborations, further propelling market expansion.

Technological advancements in generative models, including large language models (LLMs) and diffusion models, have also accelerated the adoption of synthetic data for NLP. These innovations enable the automated generation of highly realistic and contextually rich text, audio, and multimodal datasets, supporting complex NLP tasks such as machine translation, named entity recognition, and intent classification. The integration of synthetic data solutions with cloud-based AI development platforms and MLOps workflows is streamlining dataset creation, curation, and validation, making it easier for organizations of all sizes to leverage synthetic data. As a result, both established enterprises and startups are embracing synthetic data to overcome data bottlenecks, enhance AI model robustness, and unlock new use cases across languages, dialects, and domains.

Regionally, North America leads the Synthetic Data for NLP market in both market share and innovation, driven by the presence of major technology firms, research institutions, and a mature AI ecosystem. Europe follows closely, supported by strong regulatory frameworks and a growing focus on ethical AI. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digital transformation, increasing AI investments, and a burgeoning startup landscape. Latin America and the Middle East & Africa are also experiencing steady adoption, particularly in sectors such as banking, telecommunications, and e-commerce. Overall, the global market is characterized by dynamic regional trends, with each geography exhibiting unique drivers, challenges, and opportunities for synthetic data adoption in NLP.

Data Type

Synthetic Data Generation for NLP Market Outlook

According to our latest research, the Global Synthetic Data Generation for NLP market size was valued at $0.68 billion in 2024 and is projected to reach $6.2 billion by 2033, expanding at a CAGR of 28.5% during 2024–2033. The primary growth driver for this market is the exponential increase in demand for high-quality, diverse, and privacy-compliant datasets to train and validate advanced natural language processing models. As organizations worldwide accelerate their adoption of AI-powered solutions, the need to overcome data scarcity and privacy concerns is pushing enterprises and research institutions to adopt synthetic data generation technologies for NLP at an unprecedented pace.

Regional Outlook

North America currently holds the largest share of the Synthetic Data Generation for NLP market, accounting for over 38% of the global market value in 2024. This dominance is attributed to the region’s mature AI ecosystem, robust technological infrastructure, and early adoption of advanced machine learning and NLP applications across industries such as BFSI, healthcare, and IT & telecommunications. The presence of leading technology firms, innovative startups, and significant R&D investments further bolster North America’s position. Additionally, progressive data privacy regulations and an increasing focus on responsible AI practices have encouraged enterprises to invest in synthetic data generation tools, ensuring compliance while maintaining model performance and accuracy. The region's universities and research institutions also play a pivotal role in driving innovation and commercialization of these technologies.

In contrast, the Asia Pacific region is emerging as the fastest-growing market, with a forecasted CAGR of 33.2% from 2024 to 2033. This remarkable growth is fueled by surging investments in AI and digital transformation initiatives, particularly in China, India, Japan, and South Korea. Governments and private enterprises across Asia Pacific are rapidly deploying NLP solutions for multilingual chatbots, sentiment analysis, and customer engagement, necessitating large volumes of domain-specific, synthetic datasets. The region’s dynamic startup ecosystem, coupled with strategic collaborations between academia and industry, is accelerating the adoption of synthetic data generation platforms. Furthermore, the increasing penetration of cloud services and the proliferation of digital content in multiple languages are driving demand for scalable, cost-effective synthetic data solutions tailored to regional linguistic nuances.

Emerging economies in Latin America and the Middle East & Africa are beginning to recognize the potential of synthetic data generation for NLP but face unique challenges. These include limited access to advanced AI infrastructure, a shortage of skilled data scientists, and fragmented regulatory frameworks. However, localized demand for automated translation, virtual assistants, and sentiment analysis in diverse languages is steadily rising, especially in sectors like government, retail, and media. Policy reforms aimed at digital innovation and data privacy are expected to gradually unlock new opportunities, although adoption rates may remain uneven due to infrastructural and educational gaps. Strategic partnerships with global technology providers and targeted government incentives could accelerate the integration of synthetic data generation technologies in these regions over the next decade.

Report Scope

AI-Generated Synthetic Tabular Dataset Market Outlook

According to our latest research, the AI-Generated Synthetic Tabular Dataset market size reached USD 1.42 billion in 2024 globally, reflecting the rapid adoption of artificial intelligence-driven data generation solutions across numerous industries. The market is expected to expand at a robust CAGR of 34.7% from 2025 to 2033, reaching a forecasted value of USD 19.17 billion by 2033. This exceptional growth is primarily driven by the increasing need for high-quality, privacy-preserving datasets for analytics, model training, and regulatory compliance, particularly in sectors with stringent data privacy requirements.

One of the principal growth factors propelling the AI-Generated Synthetic Tabular Dataset market is the escalating demand for data-driven innovation amidst tightening data privacy regulations. Organizations across healthcare, finance, and government sectors are facing mounting challenges in accessing and sharing real-world data due to GDPR, HIPAA, and other global privacy laws. Synthetic data, generated by advanced AI algorithms, offers a solution by mimicking the statistical properties of real datasets without exposing sensitive information. This enables organizations to accelerate AI and machine learning development, conduct robust analytics, and facilitate collaborative research without risking data breaches or non-compliance. The growing sophistication of generative models, such as GANs and VAEs, has further increased confidence in the utility and realism of synthetic tabular data, fueling adoption across both large enterprises and research institutions.

Another significant driver is the surge in digital transformation initiatives and the proliferation of AI and machine learning applications across industries. As businesses strive to leverage predictive analytics, automation, and intelligent decision-making, the need for large, diverse, and high-quality datasets has become paramount. However, real-world data is often siloed, incomplete, or inaccessible due to privacy concerns. AI-generated synthetic tabular datasets bridge this gap by providing scalable, customizable, and bias-mitigated data for model training and validation. This not only accelerates AI deployment but also enhances model robustness and generalizability. The flexibility of synthetic data generation platforms, which can simulate rare events and edge cases, is particularly valuable in sectors like finance and healthcare, where such scenarios are underrepresented in real datasets but critical for risk assessment and decision support.

The rapid evolution of the AI-Generated Synthetic Tabular Dataset market is also underpinned by technological advancements and growing investments in AI infrastructure. The availability of cloud-based synthetic data generation platforms, coupled with advancements in natural language processing and tabular data modeling, has democratized access to synthetic datasets for organizations of all sizes. Strategic partnerships between technology providers, research institutions, and regulatory bodies are fostering innovation and establishing best practices for synthetic data quality, utility, and governance. Furthermore, the integration of synthetic data solutions with existing data management and analytics ecosystems is streamlining workflows and reducing barriers to adoption, thereby accelerating market growth.

Regionally, North America dominates the AI-Generated Synthetic Tabular Dataset market, accounting for the largest share in 2024 due to the presence of leading AI technology firms, strong regulatory frameworks, and early adoption across industries. Europe follows closely, driven by stringent data protection laws and a vibrant research ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, government initiatives, and increasing investments in AI research and development. Latin America and the Middle East & Africa are also witnessing growing interest, particularly in sectors like finance and government, though market maturity varies across countries. The regional landscape is expected to evolve dynamically as regulatory harmonization, cross-border data collaboration, and technological advancements continue to shape market trajectories globally.

Synthetic Legal Contract Dataset — Powered by Syncora

High-Fidelity Synthetic Dataset for LLM Training, Legal NLP & AI Research

About This Dataset

This repository provides a synthetic dataset of legal contract Q&A interactions, modeled after real-world corporate filings (e.g., SEC 8-K disclosures). The data is generated using Syncora.ai, ensuring privacy-safe, fake data that is safe to use in LLM training, benchmarking, and experimentation.

This free dataset captures the style and structure of legal exchanges without exposing any confidential or sensitive client information.

Dataset Context & Features

🚨 Simulated Regulatory Scenarios

This synthetic legal dataset is not just for LLM training — it enables developers and researchers to create simulated regulatory scenarios. Examples include:

• Detecting high-risk clauses in contracts before real-world deployment
• Testing AI models on rare or edge-case compliance situations
• Simulating SEC filings and corporate disclosures to evaluate NLP models
• Benchmarking contract analysis tools safely without exposing sensitive data

This section gives the dataset a practical, standout value, showing it can be used for stress-testing AI in legal environments.

Why Syncora?

Syncora.ai creates synthetic datasets optimized for LLM training with:

• High similarity to real-world distributions
• Free dataset access for research and open innovation
• 0% privacy leakage — fully synthetic fake data
• Robust benchmarking potential for AI & legal NLP tasks

🔗 Generate Your Own Synthetic Data

Take your AI projects further with Syncora.ai:
→ Generate your own synthetic datasets now

📜 License

This dataset is released under the MIT License.

It is 100% synthetic, safe for LLM training, and ideal for research, experimentation, and open-source projects.

Title: Rule-based Synthetic Data for Japanese GEC. Dataset Contents:This dataset contains two parallel corpora intended for the training and evaluating of models for the NLP (natural language processing) subtask of Japanese GEC (grammatical error correction). These are as follows:Synthetic Corpus - synthesized_data.tsv. This corpus file contains 2,179,130 parallel sentence pairs synthesized using the process described in [1]. Each line of the file consists of two sentences delimited by a tab. The first sentence is the erroneous sentence while the second is the corresponding correction.These paired sentences are derived from data scraped from the keyword-lookup site

Veterinary Synthetic Data Generation for AI Market Outlook

According to our latest research, the global veterinary synthetic data generation for AI market size reached USD 312 million in 2024, with a robust recorded CAGR of 22.7% over the past year. The market’s rapid growth is propelled by the increasing adoption of artificial intelligence and machine learning tools in veterinary healthcare, which demand vast, high-quality datasets for training and validation. By 2033, the market is forecasted to expand to USD 2.36 billion, reflecting the transformative impact of synthetic data on veterinary diagnostics, treatment planning, and research as per our comprehensive analysis.

The remarkable growth trajectory of the veterinary synthetic data generation for AI market is underpinned by several key factors, chief among them being the exponential rise in demand for advanced AI-driven solutions in animal healthcare. Veterinary professionals are increasingly reliant on AI models for disease diagnosis, treatment planning, and medical imaging, yet the availability of high-quality, annotated datasets in veterinary medicine remains a significant bottleneck. Synthetic data generation addresses this gap by providing scalable, diverse, and privacy-compliant datasets, enabling the development and deployment of robust AI algorithms. This is particularly critical in rare disease scenarios or underrepresented animal populations where real-world data is scarce or difficult to obtain. As the veterinary sector continues to digitize, the role of synthetic data in accelerating AI innovation is becoming ever more central.

Another major growth driver is the surge in research and development (R&D) activities within the veterinary pharmaceutical and biotechnology sectors. Companies are leveraging synthetic data to simulate clinical trials, model disease progression, and optimize drug discovery pipelines, significantly reducing time-to-market and R&D costs. The ability to generate synthetic datasets that accurately mimic real-world animal health scenarios allows for more comprehensive preclinical testing and validation of AI models, thereby enhancing the safety and efficacy of new veterinary therapeutics. Furthermore, regulatory agencies are increasingly recognizing the value of synthetic data in augmenting traditional evidence, which is fostering broader acceptance and integration of these technologies across the industry.

The proliferation of cloud computing and advancements in data generation algorithms have also played a pivotal role in market expansion. Cloud-based platforms offer scalable, cost-effective infrastructure for generating, storing, and sharing synthetic veterinary data, making these solutions accessible to organizations of all sizes. Innovations in generative adversarial networks (GANs), natural language processing (NLP), and image synthesis are enabling the creation of highly realistic and diverse synthetic datasets, which are crucial for training AI models to generalize across species, breeds, and clinical presentations. This technological progress is driving adoption not only among large veterinary hospitals and research institutes but also among smaller clinics and startups, democratizing access to AI-powered veterinary care.

From a regional perspective, North America continues to lead the veterinary synthetic data generation for AI market, accounting for the largest share in 2024 due to its advanced veterinary healthcare infrastructure and strong presence of AI technology providers. Europe follows closely, driven by robust R&D investments and supportive regulatory frameworks. The Asia Pacific region is emerging as a high-growth market, propelled by increasing pet ownership, rising livestock populations, and growing awareness of AI’s potential in veterinary medicine. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, as digital transformation initiatives gain momentum. Each region presents unique opportunities and challenges, reflecting varying levels of technological maturity, regulatory readiness, and market demand.

Component Analysis

The component segment of the veterinary synthetic data generation for AI market is bifurcated into software and services, each playing a distinct yet complementary role in enabling the adoption and utilization of synthetic data solutions. Software platforms are at the core of synthetic data generation, offering advanced tools for data creation, manipulation,

Natural Language Processing can help to unlock knowledge in the vast troves of unstructured clinical data that are collected during patient care. Patient confidentiality presents a barrier to the sharing and analysis of such data, however, meaning that only small, fragmented and sequestered datasets are available for research. To help side-step this roadblock, we explore the use of Transformer models for the generation of synthetic notes. We demonstrate how models trained on notes from the MIMIC-III clinical database can be used to generate synthetic data with potential to support downstream research studies. We release these trained models to the research community to stimulate further research in this area.

APISCRAPY's AI & ML training data is meticulously curated and labelled to ensure the best quality. Our training data comes from a variety of areas, including healthcare and banking, as well as e-commerce and natural language processing.

This dataset simulates a freelance job platform with 1,000 synthetic job postings, designed to support a wide range of machine learning tasks. It includes both structured and unstructured data, making it suitable for NLP, classification, regression, and feature engineering exercises.

• Dataset Highlights:
• Rich NLP Field: Each job includes a detailed job_description generated using contextual prompts and synthetic paragraphs.
• Structured Features: Includes budget, duration, number of applicants, hire status, freelancer rating, completion time, and success flag.
• Realistic Distributions:
• budget_usd follows a clipped normal distribution centered around $500.
• duration_days is modeled with an exponential distribution favoring short-term projects.
• num_applicants uses a Poisson distribution to simulate realistic application counts.
• Conditional Logic: Ratings and completion times are only present for hired jobs. Success is conditional on hire status.

• Categories & Titles: Jobs span 7 categories (Design, Development, Writing, Marketing, Data Science, Translation, Video Editing) with diverse titles.

• Potential Use Cases:

• NLP Tasks: Topic modeling, keyword extraction, TF-IDF or embeddings-based classification.

• Classification: Predicting hired or success based on job features and descriptions.

• Regression: Estimating budget_usd or completion_time_days from structured and textual inputs.

• Feature Importance: Analyze which features drive hiring decisions or successful completions.

• Pipeline Testing: Ideal for building robust ML pipelines with missing values, mixed data types, and realistic edge cases.

• Synthetic Nature: This dataset is entirely synthetic and was generated using controlled probabilistic distributions and the Faker library. It does not contain any real user or platform data. The goal is to provide a realistic, reproducible dataset for educational and experimental purposes.

• Metadata:

• Rows: 1,000

• Format: CSV

• License: CC BY 4.0

• Last Updated: November 2025

Customer Support Conversation Dataset — Powered by Syncora.ai

High-quality synthetic dataset for chatbot training, LLM fine-tuning, and AI research in conversational systems.

About This Dataset

This dataset provides a fully synthetic collection of customer support interactions, generated using Syncora.ai’s synthetic data generation engine.
It mirrors realistic support conversations across e-commerce, banking, SaaS, and telecom domains, ensuring diversity, context depth, and privacy-safe realism.

Each conversation simulates multi-turn dialogues between a customer and a support agent, making it ideal for training chatbots, LLMs, and retrieval-augmented generation (RAG) systems.

This is a free dataset, designed for LLM training, chatbot model fine-tuning, and dialogue understanding research.

Dataset Context & Features

Use Cases

• Chatbot Training & Evaluation – Build and fine-tune conversational agents with realistic dialogue data.
• LLM Training & Alignment – Use as a dataset for LLM training on dialogue tasks.
• Customer Support Automation – Prototype or benchmark AI-driven support systems.
• Dialogue Analytics – Study sentiment, escalation patterns, and domain-specific behavior.
• Synthetic Data Research – Validate synthetic data generation pipelines for conversational systems.

Why Synthetic?

• Privacy-Safe – No real user data; fully synthetic and compliant.
• Scalable – Generate millions of conversations for LLM and chatbot training.
• Balanced & Bias-Controlled – Ensures diversity and fairness in training data.
• Instantly Usable – Pre-structured and cleanly labeled for NLP tasks.

Generate Your Own Synthetic Data

Use Syncora.ai to generate synthetic conversational datasets for your AI or chatbot projects:
Try Synthetic Data Generation tool

License

This dataset is released under the MIT License.
It is fully synthetic, free, and safe for LLM training, chatbot model fine-tuning, and AI research.

Summary

databricks-dolly-15k is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization.

This dataset can be used for any purpose, whether academic or commercial, under the terms of the Creative Commons Attribution-ShareAlike 3.0 Unported License.

Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation

Languages: English Version: 1.0

Owner: Databricks, Inc.

Dataset Overview

databricks-dolly-15k is a corpus of more than 15,000 records generated by thousands of Databricks employees to enable large language models to exhibit the magical interactivity of ChatGPT. Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories, including the seven outlined in the InstructGPT paper, as well as an open-ended free-form category. The contributors were instructed to avoid using information from any source on the web with the exception of Wikipedia (for particular subsets of instruction categories), and explicitly instructed to avoid using generative AI in formulating instructions or responses. Examples of each behavior were provided to motivate the types of questions and instructions appropriate to each category.

Halfway through the data generation process, contributors were given the option of answering questions posed by other contributors. They were asked to rephrase the original question and only select questions they could be reasonably expected to answer correctly.

For certain categories contributors were asked to provide reference texts copied from Wikipedia. Reference text (indicated by the context field in the actual dataset) may contain bracketed Wikipedia citation numbers (e.g. [42]) which we recommend users remove for downstream applications.

Intended Uses

While immediately valuable for instruction fine tuning large language models, as a corpus of human-generated instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods outlined in the Self-Instruct paper. For example, contributor--generated prompts could be submitted as few-shot examples to a large open language model to generate a corpus of millions of examples of instructions in each of the respective InstructGPT categories.

Likewise, both the instructions and responses present fertile ground for data augmentation. A paraphrasing model might be used to restate each prompt or short responses, with the resulting text associated to the respective ground-truth sample. Such an approach might provide a form of regularization on the dataset that could allow for more robust instruction-following behavior in models derived from these synthetic datasets.

Dataset

Purpose of Collection

As part of our continuing commitment to open source, Databricks developed what is, to the best of our knowledge, the first open source, human-generated instruction corpus specifically designed to enable large language models to exhibit the magical interactivity of ChatGPT. Unlike other datasets that are limited to non-commercial use, this dataset can be used, modified, and extended for any purpose, including academic or commercial applications.

Sources

• Human-generated data: Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories.
• Wikipedia: For instruction categories that require an annotator to consult a reference text (information extraction, closed QA, summarization) contributors selected passages from Wikipedia for particular subsets of instruction categories. No guidance was given to annotators as to how to select the target passages.

Annotator Guidelines

To create a record, employees were given a brief description of the annotation task as well as examples of the types of prompts typical of each annotation task. Guidelines were succinct by design so as to encourage a high task completion rate, possibly at the cost of rigorous compliance to an annotation rubric that concretely and reliably operationalizes the specific task. Caveat emptor.

The annotation guidelines for each of the categories are as follows:

• Creative Writing: Write a question or instruction that requires a creative, open-ended written response. The instruction should be reasonable to ask of a person with general world knowledge and should not require searching. In this task, your prompt should give very specific instructions to follow. Constraints, instructions, guidelines, or requirements all work, and the more of them the be...

Synthetic Knowledge Generation Market Outlook

According to our latest research, the global Synthetic Knowledge Generation market size reached USD 2.98 billion in 2024, reflecting robust adoption across diverse sectors. The market is projected to grow at a CAGR of 32.6% during the forecast period, reaching an estimated USD 32.1 billion by 2033. This remarkable expansion is primarily driven by rapid advancements in artificial intelligence, increasing demand for data-driven decision-making, and the growing necessity for synthetic data in privacy-sensitive industries.

The primary growth factor fueling the Synthetic Knowledge Generation market is the intensifying need for high-quality, scalable, and privacy-compliant data for training advanced AI models. Traditional data collection methods often face challenges such as scarcity, high costs, and compliance with stringent data protection regulations like GDPR and CCPA. Synthetic knowledge generation addresses these challenges by creating artificial datasets that mimic real-world scenarios without compromising sensitive information. This capability is proving crucial for sectors like healthcare and finance, where data privacy is paramount, and is enabling organizations to accelerate innovation, reduce operational costs, and achieve faster time-to-market for AI-driven solutions.

Another significant driver is the surge in digital transformation initiatives across industries. As enterprises increasingly adopt AI and machine learning technologies, the demand for large, varied, and unbiased datasets has skyrocketed. Synthetic knowledge generation tools are enabling organizations to simulate complex processes, test algorithms under diverse conditions, and enhance the performance of AI systems. The proliferation of cloud computing and the integration of synthetic data platforms with existing IT infrastructures further amplify the scalability and accessibility of these solutions, making them indispensable for both large enterprises and SMEs aiming to remain competitive in the digital era.

Furthermore, the rise of generative AI technologies and advancements in natural language processing (NLP) and computer vision are propelling the adoption of synthetic knowledge generation across new application domains. Industries such as media and entertainment are leveraging these technologies to create hyper-realistic simulations and virtual environments, while the education sector is utilizing synthetic content for personalized learning experiences. The convergence of AI, big data analytics, and cloud-based deployment models is expected to unlock new opportunities for market players, fostering innovation and driving sustained growth throughout the forecast period.

From a regional perspective, North America currently leads the Synthetic Knowledge Generation market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of major technology providers, early adoption of AI, and strong regulatory frameworks supporting data privacy are key factors underpinning North America's dominance. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, expanding IT infrastructure, and increasing investments in AI research and development. Europe continues to witness steady growth, bolstered by robust government initiatives and a thriving ecosystem of AI startups. Latin America and the Middle East & Africa are also showing promising potential, albeit from a smaller base, as enterprises in these regions increasingly recognize the value of synthetic knowledge generation in addressing local data challenges.

Component Analysis

The Synthetic Knowledge Generation market is segmented by component into software, hardware, and services, each playing a pivotal role in the overall ecosystem. The software segment currently holds the largest market share, driven by the widespread adoption of advanced AI platforms and synthetic data generation tools. These software solutions offer a range of functionalities, from data synthesis and augmentation to scenario simulation and knowledge extraction, enabling organizations to generate high-fidelity synthetic datasets tailored to specific use cases. The continuous evolution of AI algorithms and the integration of machine learning pipelines have further enhanced the capabilities of these software platforms, making them indispensable for enterprises seeking to accelerate their AI initiatives.

Clinical letters contain sensitive information, limiting their use in model training, medical research, and education. This study aims to generate reliable, diverse, and de-identified synthetic clinical letters to support these tasks. We investigated multiple pre-trained language models for text masking and generation, focusing on Bio_ClinicalBERT, and applied different masking strategies. Evaluation included qualitative and quantitative assessments, downstream named entity recognition (NER) tasks, and clinically focused evaluations using BioGPT and GPT-3.5-turbo. The experiments show: (1) encoder-only models perform better than encoder–decoder models; (2) models trained on general corpora perform comparably to clinical-domain models if clinical entities are preserved; (3) preserving clinical entities and document structure aligns with the task objectives; (4) Masking strategies have a noticeable impact on the quality of synthetic clinical letters: masking stopwords has a positive impact, while masking nouns or verbs has a negative effect; (5) The BERTScore should be the primary quantitative evaluation metric, with other metrics serving as supplementary references; (6) Contextual information has only a limited effect on the models' understanding, suggesting that synthetic letters can effectively substitute real ones in downstream NER tasks; (7) Although the model occasionally generates hallucinated content, it appears to have little effect on overall clinical performance. Unlike previous research, which primarily focuses on reconstructing original letters by training language models, this paper provides a foundational framework for generating diverse, de-identified clinical letters. It offers a direction for utilizing the model to process real-world clinical letters, thereby helping to expand datasets in the clinical domain. Our codes and trained models are available at https://github.com/HECTA-UoM/Synthetic4Health.

📰 English Fake News Detection Dataset (Synthetic, 2,212 rows)

📌 Dataset Summary

This is a synthetically generated but realistic dataset created for the purpose of training and evaluating machine learning models to detect fake vs real news articles in English. The dataset mimics real-world news reporting formats and includes fabricated content with varied sources and tones.

📊 Dataset Size

• Rows (News Articles): 2,212

• Columns: 5

  • news_id: Unique identifier for each news article
  • headline: The title or headline of the article
  • body_text: The main content/body of the news
  • source: The source or publisher of the article (e.g., BBC, Unknown News)
  • label: Ground truth label — either "Fake" or "Real"

📁 Column Descriptions

🔍 Use Cases

• Fake news detection using machine learning or NLP
• Feature engineering on combined text fields (headline + body)
• Model comparison: TF-IDF + RandomForest vs Deep Learning (LSTM, BERT)
• Real vs Fake content classification using classical and modern techniques

💡 Why This Dataset?

• Clean, ready-to-use structure for binary classification tasks
• Simulates realistic headline–body–source combinations
• Can be expanded into multilingual datasets (Bangla, etc.)
• Great for building ML/NLP portfolios

📚 Example Use Case (ML Pipeline)

1. Combine headline + body_text as input features
2. Vectorize using TF-IDF or Word Embeddings

3. Train classifiers like:

   • Random Forest
   • Logistic Regression
   • LSTM / GRU
   • BERT (fine-tuning with HuggingFace)

⚠️ Note

This dataset is synthetic and should not be used for production-level decision-making. It is meant solely for research, academic projects, and model experimentation.

Synthetic Corpora Expansion Market Outlook

According to our latest research, the global synthetic corpora expansion market size reached USD 1.38 billion in 2024, reflecting robust momentum in the adoption of artificial data generation technologies. The market is projected to grow at a CAGR of 29.5% from 2025 to 2033, resulting in a forecasted value of USD 13.55 billion by 2033. This impressive growth trajectory is primarily driven by the escalating demand for high-quality, diverse, and scalable datasets to power advanced artificial intelligence (AI) and machine learning (ML) models across various industries.

One of the primary growth factors propelling the synthetic corpora expansion market is the increasing reliance on AI-driven applications that require vast and varied datasets for effective training. As organizations strive to enhance the accuracy and reliability of their AI models, the limitations of real-world data—such as privacy concerns, scarcity, and labeling costs—have become more pronounced. Synthetic corpora offer a viable solution by generating artificial datasets that mimic real-world data distributions while addressing issues of data privacy and accessibility. This capability is especially critical in regulated sectors like healthcare and finance, where data sensitivity and compliance requirements are paramount. The scalability and flexibility of synthetic data generation tools further support rapid experimentation and model iteration, fueling widespread adoption in research and enterprise environments alike.

Another significant driver for the synthetic corpora expansion market is the rapid evolution of natural language processing (NLP), speech recognition, and machine translation technologies. These applications rely heavily on large volumes of annotated data, which are often difficult and expensive to obtain in sufficient quantities. Synthetic corpora enable organizations to augment their existing datasets, improve model generalization, and reduce the risk of bias by introducing controlled variations and rare linguistic patterns. The integration of synthetic data generation into AI development pipelines also accelerates time-to-market for innovative solutions, as it minimizes dependency on manual data collection and annotation. As the sophistication of generative models continues to advance, the quality and utility of synthetic corpora are expected to improve, further expanding their role in AI research and deployment.

The growing emphasis on data augmentation and the democratization of AI technologies are also contributing to market expansion. Startups, academic institutions, and enterprises of all sizes are leveraging synthetic corpora to overcome data scarcity and enhance the robustness of their AI models. The proliferation of open-source frameworks, cloud-based platforms, and commercial synthetic data generation services has lowered the barrier to entry, enabling a broader range of organizations to experiment with and benefit from synthetic corpora. This trend is particularly evident in emerging markets, where access to large-scale real-world datasets may be limited. As regulatory scrutiny around data privacy intensifies, the adoption of synthetic corpora is poised to become a strategic imperative for organizations seeking to innovate responsibly and maintain a competitive edge.

Regionally, North America remains the dominant force in the synthetic corpora expansion market, accounting for the largest share of global revenue in 2024. The region's leadership is underpinned by a mature AI ecosystem, significant investments in research and development, and a high concentration of technology giants and startups. Europe and Asia Pacific are also witnessing rapid growth, driven by increasing digital transformation initiatives, supportive government policies, and a burgeoning talent pool in data science and AI. While Latin America and the Middle East & Africa currently represent smaller market shares, these regions are expected to post above-average growth rates over the forecast period as local industries embrace AI-driven innovation and synthetic data solutions.

Component Analysis

The synthetic corpora expansion market is segmented by component into software, services, and platforms. The software segment holds a significant share of the market, driven by the continuous development of advanced tools for synthetic data generation, annotation, and validation. These software solutions are designed to c

This dataset contains detailed records of IT service tickets, combining structured metadata (such as priority, category, and assignment) with rich ticket descriptions suitable for natural language processing. It enables automated ticket triage, prioritization, and advanced analytics for IT support operations, making it ideal for machine learning and process optimization.

This dataset contains synthetic radiology report texts with structured fields for patient demographics, imaging modality, body part examined, and detailed report content. It includes optional annotations for clinical entities, report classifications, and summaries, making it ideal for developing and benchmarking NLP models in medical imaging. The dataset supports entity recognition, classification, and summarization tasks, facilitating research in healthcare AI and clinical decision support.

📘 Overview

This dataset contains 300,000 synthetic flashcards and notes across multiple subjects — including Mathematics, Computer Science, Physics, Biology, and Chemistry.
Each record includes a source text, summary, and question–answer pair, labeled with difficulty level, Bloom’s taxonomy level, cognitive skill, language, and source type.

🧠 Why This Dataset

Education and AI are merging rapidly. This dataset was created to help researchers and developers:

• Train and evaluate NLP models for question answering, summarization, and classification
• Build EdTech applications (quiz generation, flashcard apps, tutoring assistants)
• Analyze student learning patterns based on topic difficulty and Bloom’s level
• Explore multi-lingual educational text generation (English, Urdu, Hindi)

📊 Dataset Structure