Ovis2 is the latest version of the Ovis series models proposed by Alibaba's international team. Compared to the previous version 1.6, Ovis2 has significant improvements in data construction and training methods. It not only enhances the capacity density of small models but also greatly improves chain of thought (CoT) reasoning capabilities through instruction fine-tuning and preference learning. Additionally, Ovis2 introduces video and multi-image processing capabilities, and enhances multilingual abilities and OCR capabilities in complex scenarios, significantly increasing the model's practicality.

Recently, Vectara released a report titled 'Hallucination Ranking', comparing the performance of different large language models (LLMs) in generating hallucinations while summarizing short documents. This ranking utilized Vectara's Hughes Hallucination Evaluation Model (HHEM-2.1), which is regularly updated to assess the frequency of false information introduced by these models in summaries. According to the latest data, the report highlighted key metrics such as hallucination rates, factual consistency rates, response rates, and average summary lengths of several popular models.

Recently, Microsoft launched OmniParser V2.0, a new parsing tool designed to convert user interface (UI) screenshots into structured formats. OmniParser enhances the performance of UI agents based on large language models (LLM), helping users better understand and interact with the information on their screens. The tool's training dataset includes an interactive icon detection dataset, meticulously curated and automatically annotated from popular websites to highlight clickable and actionable areas.

Hugging Face has recently launched an online learning course called 'Agent Course', aimed at helping learners deepen their understanding of the fundamentals and applications of intelligent agents. The course is rich in content and is divided into five modules, progressing from basic concepts of agents to final assessment assignments, assisting students in mastering the necessary skills. The first module is 'Welcome to the Course', providing an overview, guidelines, and required tools to ensure that learners have a solid foundation during their studies. Next is

Recently, Tencent Technology (Shenzhen) Co., Ltd. published a patent regarding a training method and related equipment for large language models on the Tianyancha app. The patent is titled 'Training Method, Device, Computer Equipment, and Storage Medium for Large Language Models' and aims to enhance the learning capacity and accuracy of large language models through innovative training methods. In the training process of large language models, traditional methods often rely on a single text summary, which may lead to model overfitting and negatively impact the accuracy and diversity of generated content. However, Tencent's new...

Realbotix, a leading manufacturer of humanoid robots and companion AI, is further expanding its technological capabilities by launching large language model (LLM) integration and advanced customization features. These new functionalities are set to debut in February 2025, allowing users to seamlessly connect with a variety of mainstream AI platforms including OpenAI's ChatGPT, Meta's Llama, Google's Gemini, and the newly launched DeepSeek R1.

In the current technological environment, artificial intelligence (AI) has sparked widespread discussion. Eerke Boiten, a professor of cybersecurity at De Montfort University, states that existing AI systems have fundamental flaws in management and reliability, and therefore should not be used in critical applications. Professor Boiten points out that most current AI systems rely heavily on large neural networks, particularly generative AI and large language models (like ChatGPT). The workings of these systems are relatively complex, even though each neural...

In the field of academic research, literature retrieval is a complex and crucial information acquisition task. Researchers need the capability to handle intricate, domain-specific search queries to meet detailed research requirements. However, existing academic search platforms, like Google Scholar, often struggle to address these complex research queries. For example, queries related to non-stationary reinforcement learning using the UCB method require stronger computational and analytical capabilities. Moreover, researchers typically need to spend a significant amount of time and effort manually browsing extensive academic literature when conducting literature reviews.

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,

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.

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.

Recently, a research team from New York University published a study revealing the vulnerabilities of large language models (LLM) in data training. They found that even a tiny amount of false information, constituting just 0.001% of the training data, can lead to significant errors in the entire model. This finding is particularly concerning in the medical field, where inaccurate information can directly impact patient safety. Image credit: Image generated by AI, image licensed by Midjourney. The researchers published their paper in the journal Nature Medicine.

In recent years, the programming capabilities of Artificial Intelligence (AI) have been continuously evolving, but they have not yet reached perfection. Recently, Max Woolf, a senior data scientist at BuzzFeed, discovered through experiments that if continuous prompts for 'writing better code' are provided to large language models (LLMs), AI can indeed generate higher quality code. This finding has garnered widespread attention, with renowned AI scientists emphasizing the importance of iteration and prompt design. In Woolf's experiment,

Driven by multimodal large language models (MLLMs), significant advancements have been made in tasks related to images and videos, including visual question answering, narrative generation, and interactive editing. However, achieving fine-grained understanding of video content still poses major challenges. These challenges involve tasks such as pixel-level segmentation, tracking with language descriptions, and visual question answering based on specific video prompts. Although current state-of-the-art video perception models excel in segmentation and tracking tasks, they still fall short in open language understanding and conversational capabilities.

As businesses increasingly adopt large language models (LLMs), improving the accuracy of model knowledge and reducing hallucination phenomena has become a significant challenge. Researchers at Meta AI have introduced a 'Scalable Memory Layer' in a recent paper, which may provide a solution to this issue. The core idea of the scalable memory layer is to add more parameters to the LLMs without increasing computational resources during inference, thereby enhancing their learning capabilities. This architecture is suitable for storing large amounts of factual knowledge while maintaining...

In recent years, training large language models (LLMs) has become increasingly expensive and complex, with only a few major tech companies possessing the necessary computing resources. However, Google has recently introduced a new method called SALT (Small Model-Assisted Large Model Training), which could fundamentally change the landscape of AI training. Image Source Note: The image was generated by AI, and the image is authorized by service provider Midjourney based on the latest research papers from Google Research and DeepMind, 'A Little Help Goes a Long Way: Leveraging Small Language Models...'

Recently, Meta has announced a groundbreaking research achievement by developing a new type of memory layer technology that can significantly enhance the factual accuracy of large language models (LLMs) and achieve unprecedented expansion in parameter scale. This technology not only challenges traditional neural network expansion methods but also provides new directions for future AI architecture design. The core of this research lies in utilizing a trainable key-value lookup mechanism to add extra parameters to the model without increasing computational load (FLOPs). This method's core thinking is...