LLMDataHub: A Collection of High-Quality Datasets for Large Language Models
Overview
LLMDataHub is a curated repository dedicated to gathering high-quality datasets essential for training large language models (LLMs). As LLMs, such as OpenAI's GPT series, Google's Bard, and Baidu's Wenxin Yiyan, continue to revolutionize technology, there is a growing interest in training these models within the open-source community. Thanks to frameworks like LlaMa and ChatGLM, smaller organizations and individuals can now participate in this training process. LLMDataHub aims to support this endeavor by assembling an array of comprehensive datasets necessary for developing effective chatbot language models.
Purpose
To train a chatbot LLM capable of following human instructions accurately, it is crucial to access diverse and high-quality datasets. These datasets significantly contribute to chatbots' conversational abilities across various domains and styles. LLMDataHub addresses this need by offering a selection of datasets specifically tailored for chatbot training. This collection includes detailed information such as links, dataset size, language, and descriptions to assist researchers and developers in selecting the most appropriate data for their projects.
Datasets Categories
The repository features different types of datasets to accommodate various training requirements:
- Alignment Datasets: Ideal for aligning the model with specific guidelines or conversational norms.
- Domain-specific Datasets: Cater to specific fields or topics, allowing models to fine-tune their expertise in targeted areas.
- Pretraining Datasets: Used for the initial training phase to help models understand and generate human-like text.
- Multimodal Datasets: Combine text with other data types like images to enhance model versatility.
Recent Releases
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November 2023
- HelpSteer: A dataset annotated with measures of helpfulness and correctness among others, useful for RLHF (Reinforcement Learning with Human Feedback).
- No Robots: Features high-quality, single-turn, human-created supervised fine-tuning data.
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September 2023
- Anthropic_HH_Golden: A refined version of Anthropics Helpful and Harmless dataset, significantly improving model performance in harmlessness metrics.
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August 2023
- AmericanStories: A large corpus sourced from the US Library of Congress for pretraining.
- Platypus: Designed to enhance reasoning ability in STEM domains.
Contribution and Contact
LLMDataHub invites contributions from the community to expand and improve the datasets available. Interested individuals can contact the project owner, Junhao Zhao, via email. The project is advised by Prof. Wanyun Cui.
Conclusion
LLMDataHub serves as an invaluable resource for anyone aiming to train or improve language models. By providing easy access to a variety of high-quality training datasets, it supports the development of more sophisticated and capable AI-driven conversational agents. Whether you are focused on dialogue quality, response generation, or language understanding, LLMDataHub offers essential tools for your research and development efforts.