AI2 Releases Open Dataset Dolma: Breaking Down Data Barriers for AI Language Models

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Published inAI News · 2 min read · Aug 21, 2023

The Allen Institute for AI (AI2) has released an open-source text dataset named Dolma, designed to enhance the transparency and innovation of AI language models. As a centerpiece of AI2's Open Language Model (OLMo) initiative, Dolma will provide researchers and developers with free access to data resources, supporting a broader range of AI research. Not only is Dolma a vast open dataset with 3 billion tokens, but it also features straightforward usage and licensing terms. AI2 has adopted the "ImpACT License for Moderate-Risk Work" and encourages users to provide contact information and usage details. The openness of this dataset offers researchers and developers more resources, propelling the AI field towards a more transparent and collaborative future.

English Literature Graduate Develops AI Tool to Easily Interpret Twitter Bios

In today's digital world, the use of short text has become central to online communication. However, these texts often lack common vocabulary or context, posing numerous challenges for Artificial Intelligence (AI) during analysis. In response, Justin Miller, an English Literature graduate student and data scientist from the University of Sydney, proposed a novel approach that utilizes Large Language Models (LLMs) to gain deeper understanding and analysis of short texts. Miller's research focuses on how to analyze a vast array of short texts, such as social media profiles,

A Breakthrough in Domestic Large Models! DeepSeek R1 Open-Sourced, Performance Rivals OpenAI, Ushering in a New Era of AI Equality

DeepSeek has officially released and open-sourced its latest large language model R1, which has shown outstanding performance and is considered comparable to OpenAI's official version o1. This initiative not only marks another significant breakthrough in domestic AI technology but also provides new options for AI developers worldwide. DeepSeek R1 has extensively applied reinforcement learning techniques in the post-training phase, significantly enhancing the model's reasoning capabilities even with minimal labeled data, in areas such as mathematics, coding, and natural language reasoning.

Breakthrough in Large Models: Extracting High-Quality Multimodal Textbooks from Educational Videos

Recently, Zhejiang University and Alibaba DAMO Academy jointly released a remarkable study aimed at creating high-quality multimodal textbooks from educational videos. This innovative research not only provides new ideas for training large-scale language models (VLMs) but may also change the way educational resources are utilized. With the rapid development of artificial intelligence technology, the pre-training corpus of VLMs mainly relies on visual-text pairs and visually intertwined data. However, much of this current data comes from the web, where the correlation between text and images is weak, and the knowledge density is relatively low.

AI Performs Poorly on Advanced History Exams: GPT-4 Turbo Achieves Only 46% Accuracy

Recently, a study led by the Austrian Institute of Complexity Science (CSH) revealed that despite large language models (LLMs) excelling in various tasks, they show significant shortcomings when tackling advanced history questions. The research team tested three leading models, including OpenAI's GPT-4, Meta's Llama, and Google's Gemini, with disappointing results. Note: The image was generated by AI, and the image is licensed by Midjourney to evaluate these models in history.