Synthetic datasets for word scramble and arithmetic tasks described in the GPT3 paper.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('gpt3', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

AI Training Data Market Outlook

As of 2023, the global AI Training Data market size is valued at approximately USD 1.5 billion, with an anticipated growth to USD 8.9 billion by 2032, driven by a robust CAGR of 21.7%. The increasing adoption of AI across various industries and the continuous advancements in machine learning algorithms are primary growth factors for this market. The demand for high-quality training data is exponentially increasing to improve AI model accuracy and performance.

One of the primary growth drivers for the AI Training Data market is the rapid technological advancements in AI and machine learning. These advancements necessitate large volumes of high-quality training data to develop and fine-tune algorithms. Companies are continuously innovating and investing in AI technologies, which in turn boosts the demand for diverse and accurate training datasets. Furthermore, AI's capability to enhance business processes, improve decision-making, and drive operational efficiency motivates industries to leverage AI, thus fueling the need for robust training data.

Another significant factor propelling the market is the widespread adoption of AI across various sectors such as healthcare, automotive, retail, and BFSI (Banking, Financial Services, and Insurance). In healthcare, AI is revolutionizing diagnostics, patient care, and administrative processes, requiring vast amounts of data for training purposes. Similarly, the automotive industry relies on AI for developing autonomous vehicles, which demand extensive labeled data for functions like object recognition and navigation. The retail industry leverages AI for personalized customer experiences, inventory management, and sales forecasting, all of which require a substantial amount of training data.

The growth of the AI Training Data market is also driven by increasing investments in AI research and development by both private organizations and governments. Governments worldwide are recognizing the potential of AI in driving economic growth and are consequently investing in AI initiatives. Private companies, particularly tech giants, are also heavily investing in AI to maintain a competitive edge. These investments are aimed at acquiring high-quality training data, developing new AI models, and enhancing existing ones, further propelling market growth.

The increasing complexity and diversity of AI applications necessitate the use of advanced Ai Data Labeling Solution. These solutions are pivotal in transforming raw data into structured and meaningful datasets, which are essential for training AI models. By employing sophisticated labeling techniques, AI data labeling solutions ensure that data is accurately annotated, thereby enhancing the model's ability to learn and make predictions. This process not only improves the quality of the training data but also accelerates the development of AI technologies across various sectors. As the demand for high-quality labeled data continues to rise, leveraging efficient data labeling solutions becomes a critical component in the AI development lifecycle.

From a regional perspective, North America dominates the AI Training Data market, owing to the significant presence of leading AI companies and substantial R&D investments. The Asia Pacific region is anticipated to exhibit the fastest growth, driven by the increasing adoption of AI technologies in countries like China, Japan, and India. Europe also holds a considerable share of the market, with strong contributions from countries such as the UK, Germany, and France. The Middle East & Africa and Latin America regions are emerging markets, gradually catching up with advancements in AI and its applications.

Data Type Analysis

The AI Training Data market is segmented by data type into text, image, audio, video, and others. Text data holds a significant share due to its extensive use in natural language processing (NLP) applications. NLP algorithms require large volumes of textual data to understand, interpret, and generate human languages. The proliferation of digital content and social media has resulted in an abundance of text data, making it a critical component of AI training datasets. Moreover, advancements in text generation models, such as GPT-3, further amplify the need for high-quality textual data.

Image data is another crucial segment, primarily driven by the increasing applications of computer vision technologies. Industrie

AI Large Language Model Market Outlook

The AI Large Language Model market size is projected to grow from USD 12.1 billion in 2023 to USD 84.3 billion by 2032, at a compound annual growth rate (CAGR) of 24.5% over the forecast period. This growth is driven by the increasing adoption of advanced AI technologies across various industries to enhance operational efficiency, customer experience, and decision-making processes.

A key driver of this market growth is the exponential increase in data generation and the need for advanced data processing capabilities. Large language models, such as GPT-3 and its successors, have demonstrated remarkable proficiency in understanding and generating human-like text, making them indispensable tools for applications requiring natural language understanding and generation. The ability of these models to perform a wide range of tasks—ranging from customer support to content creation and beyond—has significantly expanded their appeal and utility in the business world.

Another significant factor contributing to the market's growth is the surging investments in AI and machine learning by both public and private sectors. Governments worldwide are recognizing the strategic importance of AI technologies and are launching various initiatives to support AI research and development. Concurrently, private companies are investing heavily in AI to gain a competitive edge, which is boosting the demand for large language models. Furthermore, advancements in computational power and cloud computing are facilitating the seamless deployment and scaling of these models, thereby driving market growth.

The increasing demand for personalized customer experiences is also propelling the adoption of AI large language models. Businesses are leveraging these models to offer customized interactions and recommendations, thereby improving customer satisfaction and loyalty. For instance, in the retail and e-commerce sectors, large language models are being used to provide personalized shopping experiences by understanding customer preferences and behavior. Similarly, in the healthcare sector, these models are assisting in providing personalized treatment plans and improving patient outcomes.

Regionally, North America holds a significant share of the AI large language model market, driven by robust technological infrastructure, high adoption rates of advanced technologies, and substantial investments in AI research. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid digitalization, increasing internet penetration, and supportive government initiatives. Europe also represents a strong market due to its focus on technological innovation and stringent data protection regulations, which drive the demand for advanced AI solutions.

Component Analysis

The AI large language model market is segmented into components such as software, hardware, and services. The software component is expected to dominate the market, driven by continuous advancements in AI algorithms and the growing need for sophisticated AI applications across various industries. The software segment includes natural language processing (NLP) tools, machine learning frameworks, and AI development platforms that enable the creation and deployment of large language models. These tools have become essential in developing applications that require text generation, translation, summarization, and other language-related tasks.

The hardware component is also witnessing significant growth, primarily due to the increasing demand for high-performance computing (HPC) systems and specialized processors such as GPUs and TPUs. These hardware solutions are crucial for training large language models, which require immense computational power. Companies are investing in advanced hardware to accelerate the training process and improve the efficiency of AI models. With the rise of AI-driven applications, the demand for scalable and efficient hardware solutions is expected to grow, further driving the hardware segment's expansion.

Services form another critical component of the AI large language model market, encompassing consulting, integration, and support services. As businesses increasingly adopt AI technologies, there is a growing need for specialized services to ensure successful implementation and integration of large language models into existing systems. Service providers offer expertise in AI strategy development, model training, deployment, and maintenance, helping organizations maximize the

Energy consumption of artificial intelligence (AI) models in training is considerable, with both GPT-3, the original release of the current iteration of OpenAI's popular ChatGPT, and Gopher consuming well over **********-megawatt hours of energy simply for training. As this is only for the training model it is likely that the energy consumption for the entire usage and lifetime of GPT-3 and other large language models (LLMs) is significantly higher. The largest consumer of energy, GPT-3, consumed roughly the equivalent of *** Germans in 2022. While not a staggering amount, it is a considerable use of energy. Energy savings through AI While it is undoubtedly true that training LLMs takes a considerable amount of energy, the energy savings are also likely to be substantial. Any AI model that improves processes by minute numbers might save hours on shipment, liters of fuel, or dozens of computations. Each one of these uses energy as well and the sum of energy saved through a LLM might vastly outperform its energy cost. A good example is mobile phone operators, of which a ***** expect that AI might reduce power consumption by *** to ******* percent. Considering that much of the world uses mobile phones this would be a considerable energy saver. Emissions are considerable The amount of CO2 emissions from training LLMs is also considerable, with GPT-3 producing nearly *** tonnes of CO2. This again could be radically changed based on the types of energy production creating the emissions. Most data center operators for instance would prefer to have nuclear energy play a key role, a significantly low-emission energy producer.

"gpt3.5-gpt4-input-output-echram.zip" :

Input and output to GPT-3.5 and GPT-4 based on ECHR dataset published in JSON format in this paper for argument component classification only i.e. clauses that are argumentative (conclusion/premise), extracted from the JSON file

Note: Output of the model is under OpenAI Terms & policies.

Please cite our paper also if you use this dataset: Performance analysis of large language models in the domain of legal argument mining

You can click here for BibTex or copy the text below.

@ARTICLE{10.3389/frai.2023.1278796,

AUTHOR={Al Zubaer, Abdullah and Granitzer, Michael and Mitrović, Jelena },

TITLE={Performance analysis of large language models in the domain of legal argument mining},

JOURNAL={Frontiers in Artificial Intelligence},

VOLUME={6},

YEAR={2023},

URL={https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1278796},

DOI={10.3389/frai.2023.1278796},

ISSN={2624-8212},

ABSTRACT={Generative pre-trained transformers (GPT) have recently demonstrated excellent performance in various natural language tasks. The development of ChatGPT and the recently released GPT-4 model has shown competence in solving complex and higher-order reasoning tasks without further training or fine-tuning. However, the applicability and strength of these models in classifying legal texts in the context of argument mining are yet to be realized and have not been tested thoroughly. In this study, we investigate the effectiveness of GPT-like models, specifically GPT-3.5 and GPT-4, for argument mining via prompting. We closely study the model's performance considering diverse prompt formulation and example selection in the prompt via semantic search using state-of-the-art embedding models from OpenAI and sentence transformers. We primarily concentrate on the argument component classification task on the legal corpus from the European Court of Human Rights. To address these models' inherent non-deterministic nature and make our result statistically sound, we conducted 5-fold cross-validation on the test set. Our experiments demonstrate, quite surprisingly, that relatively small domain-specific models outperform GPT 3.5 and GPT-4 in the F1-score for premise and conclusion classes, with 1.9% and 12% improvements, respectively. We hypothesize that the performance drop indirectly reflects the complexity of the structure in the dataset, which we verify through prompt and data analysis. Nevertheless, our results demonstrate a noteworthy variation in the performance of GPT models based on prompt formulation. We observe comparable performance between the two embedding models, with a slight improvement in the local model's ability for prompt selection. This suggests that local models are as semantically rich as the embeddings from the OpenAI model. Our results indicate that the structure of prompts significantly impacts the performance of GPT models and should be considered when designing them.}}

The AI content detection market is experiencing rapid growth, driven by the proliferation of AI-generated content and increasing concerns regarding plagiarism, academic dishonesty, and misinformation. The market, estimated at $250 million in 2025, is projected to experience a robust Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $1.5 billion by 2033. This expansion is fueled by several key factors. Firstly, the rise of sophisticated AI writing tools like GPT-3 and others necessitates the development of equally advanced detection mechanisms. Secondly, educational institutions, news organizations, and businesses are increasingly adopting AI detection tools to ensure the authenticity and originality of content, fostering trust and maintaining academic integrity. Thirdly, evolving regulatory landscapes are pushing for greater transparency and accountability regarding AI-generated content, further stimulating market demand. The market segmentation reveals a strong emphasis on text content detection, which currently dominates, but the image and video content detection segments are showing significant growth potential as AI-generated media become more prevalent. The market’s growth is not without challenges. The evolving nature of AI algorithms, coupled with the potential for adversarial attacks aimed at circumventing detection, represents a key restraint. Furthermore, the accuracy and reliability of detection tools remain crucial concerns, requiring continuous improvement in algorithms and training data. Competitive landscape is also intensifying as numerous companies are entering this space, leading to price competition and a focus on differentiating features. Nevertheless, the overall trend points towards significant market expansion as AI content generation continues its rapid evolution and the need for robust detection mechanisms increases across various sectors. North America currently holds a significant market share, owing to early adoption and strong regulatory frameworks, but the Asia Pacific region is anticipated to witness the fastest growth in the coming years due to increasing digital literacy and technological advancements.

Dataset Card for Alpaca-Cleaned

Repository: https://github.com/gururise/AlpacaDataCleaned

  Dataset Description

This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:

Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.

"instruction":"Summarize the… See the full description on the dataset page: https://huggingface.co/datasets/yahma/alpaca-cleaned.

Introduction

This repository holds the data file for translating TechLinked, which talks about mostly technology and science news. Raw data is in the data/ folder. Scripts generate OpenAI's ChatCompletion Fine-tuning API formatted training data in jsonl format. -2000 variants are designed to be used with GPT-3 with 8192 tokens context length limit. -8192 variants are designed to be used with GPT-4o mini with 128000 context window and 16384 max output tokens.

  How to add… See the full description on the dataset page: https://huggingface.co/datasets/metricv/metricsubs-chunktranslate.

This dataset is a combination of Stanford's Alpaca (https://github.com/tatsu-lab/stanford_alpaca) and FiQA (https://sites.google.com/view/fiqa/) with another 1.3k pairs custom generated using GPT3.5 Script for tuning through Kaggle's (https://www.kaggle.com) free resources using PEFT/LoRa: https://www.kaggle.com/code/gbhacker23/wealth-alpaca-lora GitHub repo with performance analyses, training and data generation scripts, and inference notebooks: https://github.com/gaurangbharti1/wealth-alpaca… See the full description on the dataset page: https://huggingface.co/datasets/gbharti/finance-alpaca.