10 Open Source datasets for LLM training
Introduction
As you may know, large language models (LLMs) are taking the world by storm, powering remarkable applications like ChatGPT, Bard, Mistral, and more. But have you ever wondered what fuels these robust AI systems? The answer lies in the vast datasets used to train them.
Just like humans learn from exposure to information, LLMs gain their knowledge and capabilities by ingesting and learning from massive collections of text data. For instance, you learn from the books and lectures teachers provide in school. Similarly, LLMs learn from vast datasets comprising books, articles, videos, websites, and more, absorbing the knowledge within them to expand their understanding and abilities. This process enables them to comprehend, generate, and interact with human language, mimicking aspects of human cognition on a digital scale. The quality, diversity, and relevance of these datasets play a crucial role in shaping the performance and capabilities of the resulting language model.
This article will explore 10 fascinating datasets instrumental in training cutting-edge LLMs. From academic papers to online forums, these datasets offer a glimpse into the diverse sources of knowledge that power these remarkable AI systems. So, let’s dive in and discover data that shape the future of language AI!
Why You Will Need Open Source Datasets?
Access to high-quality training data is crucial for developing powerful language models in AI. While tech giants like Google, OpenAI, and Anthropic have the resources to curate proprietary datasets, most researchers and startups may not have the same luxury. This is where open source datasets for LLM training come into play, offering a democratized avenue for training cutting-edge language models. Here’s why you might need open source datasets for training large language models:
Cost-effectiveness
Assembling massive datasets from scratch can be prohibitively expensive, especially for smaller organizations or independent researchers. Open source datasets for LLM training provide a cost-effective solution, enabling access to diverse, high-quality data without substantial financial investment.
Reproducibility and transparency
Science thrives on reproducibility, and open source datasets facilitate this principle. Using publicly available data, researchers can replicate experiments, validate findings, and build upon existing work, fostering a collaborative and transparent research environment.
Diversity and inclusivity
Open source datasets often encompass various topics, styles, and perspectives, which can help mitigate biases and promote inclusivity in language models. This diversity is essential for developing AI systems that can understand and communicate effectively with diverse populations.
Rapid iteration and innovation
With open access to data, researchers can rapidly iterate and experiment with different model architectures, training techniques, and fine-tuning approaches, accelerating innovation in natural language processing.
Fostering community and collaboration
Open source datasets for LLM training encourage a vibrant community of researchers, developers, and enthusiasts to collaborate, share insights, and collectively advance the state of the art in language AI.
While proprietary datasets may offer certain advantages, the open source approach democratizes access to knowledge, promotes transparency, and fosters a culture of collaboration that can drive breakthroughs in language AI for the benefit of society.
Also read: Beginners’ Guide to Finetuning Large Language Models (LLMs)
10 Open Source datasets for LLM training
Here are 10 open source datasets for LLM training:
LAION-2B-en (text and image Dataset)
Research Paper Link: Access Here
Official Link: Access Here
Introducing LAION-5B, a groundbreaking open dataset designed to fuel the next generation of image-text models. A staggering 5.85 billion CLIP-filtered image-text pairs dwarf previous datasets like LAION-400M, facilitating unprecedented training opportunities. This dataset revolutionizes language-vision architectures by eliminating the need for costly, accurate labels typical in standard vision-supervised learning. Models trained on LAION-5B exhibit remarkable text-guided image generation capabilities and excel in zero-shot classification with remarkable out-of-distribution robustness.
Notable advancements in language-vision models like ALIGN, BASIC, GLIDE, Flamingo, and Imagen owe their progress to datasets of this scale. LAION-5B enables replication and fine-tuning of foundational models and facilitates further experimentation and research within the broader community. Moreover, it offers enhanced features such as nearest neighbor indices, improved web interfaces, and detection scores for watermark, NSFW, and toxic content, democratizing research in multi-modal AI.
cognitivecomputations/dolphin
Hugging Face Link: Access Here
Official Link: Access Here
The Dolphin dataset is a replication attempt of Microsoft’s Orca, built upon FLANv2. It comprises approximately 1 million instances of FLANv2 augmented with GPT-4 completions and around 3.5 million instances augmented with GPT-3.5 completions. Unlike the original Orca, all 75k instances of CoT are included in the FLAN-1m dataset, and duplicates have been removed, resulting in 3.5 million instructions in the ChatGPT dataset. To ensure an uncensored model suitable for personalized alignment LoRA, instances of alignment, refusal, avoidance, and bias have been filtered out. The token distribution for GPT-3.5 completions is also provided, aiming to enhance language model capabilities while maintaining integrity and neutrality.
xP3 (text and code)
Research Paper Link: Access Here
Hugging Face Link: Access Here
xP3, or Cross-lingual Public Pool of Prompts, is a diverse repository featuring prompts and datasets spanning 46 languages and addressing 16 NLP tasks. It is a vital resource for training cutting-edge multilingual language models like BLOOMZ and mT0. Thanks to the extensive and varied data in xP3, these models can comprehend and execute human instructions across numerous languages without specific training.
P3 (text, prompts)
Hugging Face Link: Access Here
Research Paper Link: Access Here
P3 (Public Pool of Prompts) encompasses an array of English datasets designed for diverse NLP tasks, structured around prompts consisting of input and target templates. These templates function as mappings, converting data examples into natural language for input and output sequences. For instance, in tasks like Natural Language Inference (NLI), input templates might frame questions like “If {Premise} is true, is it also true that {Hypothesis}?”. In contrast, target templates define options such as Choices[label]. Promptsource facilitates creating and curating these prompts, allowing interactive prompt writing and metadata collection, including evaluation metrics. With over 2,000 prompts gathered from 270+ datasets, P3 fosters task-specific training and evaluation. The dataset, publicly accessible via Promptsource, enables reproducible research and advances in zero-shot task generalization, as demonstrated in Multitask Prompted Training Enables Zero-Shot Task Generalization.
Common Crawl
Official Website: Access Here
Research Paper: Access Here
Common Crawl offers an extensive, freely accessible archive of web crawl data for universal use. With a repository spanning over 250 billion pages gathered over 17 years, it has been an open corpus since 2007. Widely recognized, it has been referenced in over 10,000 research papers, making it a valuable resource for diverse fields such as natural language processing, machine learning, and information retrieval.
COYO-700M (text and image)
Hugging Face Link: Access Here
Research Paper: Access Here
The COYO-700M dataset is an extensive collection comprising 747 million pairs of images and text and additional meta-attributes, enhancing its versatility for training various models. Inspired by previous vision-and-language datasets, COYO-700M gathers informative pairs of alt-text and corresponding images from HTML documents. This dataset is designed to support training large-scale foundation models, complementing existing resources in the field.
VIMA dataset (text and image)
Research Paper Link: Access Here
GitHub Link: Access Here
The VIMA dataset represents a groundbreaking advancement in prompt-based learning for robotics. Unlike traditional approaches that rely on specialized models for different tasks, VIMA demonstrates the effectiveness of a single, versatile language model instructed through multimodal prompts combining text and visuals. This dataset includes thousands of procedurally generated tabletop tasks with accompanying multimodal prompts, over 600,000 expert trajectories for imitation learning, and a comprehensive four-level evaluation protocol for systematic generalization. VIMA’s transformer-based robot agent efficiently processes these prompts, generating motor actions autoregressively. Notably, VIMA achieves remarkable scalability and data efficiency, outperforming alternative designs by up to 2.9 times in zero-shot generalization with the same training data and maintaining a 2.7 times performance lead with only one-tenth of the training data.
RefinedWeb
Research Paper Link: Access Here
Hugging Face Link: Access Here
RefinedWeb is a colossal compilation of meticulously filtered and deduplicated tokens from the extensive Common Crawl dataset. Tokens, representing meaningful units of language in NLP, form this repository’s building blocks, boasting over 5 trillion tokens in total. A substantial subset of 600 billion tokens has been made openly accessible. This initiative was propelled to facilitate the training of models like Falcon-40B, emphasizing the utilization of smaller yet refined datasets of exceptional quality. Contrary to previous assumptions, RefinedWeb underscores the remarkable potency achievable through well-filtered web data alone, showcasing superior performance compared to models trained on The Pile, a benchmark in the field.
The Pile
Official Website Link: Access Here
Hugging Face Link: Access Here
RefinedWeb, an 800 GB dataset curated from 22 diverse sources, enhances the generalization capabilities of large language models like GPT-Neo, LLaMA, and OPT. Recent studies highlight the importance of data source diversity in improving models’ comprehension across various domains. Models trained on RefinedWeb show improvements in conventional benchmarks and excel in Pile BPB, a cross-domain text modeling ability measure. Pile BPB assesses a model’s proficiency in understanding diverse domains such as books, GitHub repositories, webpages, chat logs, and scholarly papers in medicine, physics, mathematics, computer science, and philosophy, validating its broad knowledge and reasoning skills.
ROOTS
Research Paper Link: Access Here
Hugging Face Link: Access Here
The ROOTS dataset is a massive multilingual collection comprising 1.6TB of text from 59 languages. Its creation aimed to support training in the BigScience Large Open-science Open-access Multilingual (BLOOM) language model. This dataset draws heavily from meticulously deduplicated and filtered sources such as Common Crawl, GitHub Code, and other community-driven initiatives. As language models expand, the demand for comprehensive text datasets grows, particularly in multilingual contexts. The BigScience workshop, an international initiative focused on ethical and responsible large language model research, spearheaded the creation of ROOTS. This corpus serves as training data for BLOOM and fosters research into large-scale monolingual and multilingual modeling. By releasing a significant subset of the dataset and analytical tools, BigScience aims to catalyze advancements in language model development and promote ethical considerations within the field.
Conclusion
In conclusion, developing large language models (LLMs) relies heavily on access to diverse, high-quality datasets. While proprietary datasets offer advantages, open source datasets for LLM training democratize access to knowledge, promote transparency, and foster collaboration, driving breakthroughs in language AI. The exploration of ten such datasets highlights their pivotal role in advancing the field and shaping the future of LLMs.
If you find any other open source datasets for LLM training, feel free to comment in the section below. We would love to hear from you.
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