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...

Against the backdrop of the global generative AI wave in 2022, YuanShi Intelligence (RWKV) completed tens of millions of RMB in angel round financing in December 2023, with investment from Tianji Capital. Following this financing, the company’s valuation doubled, and the funds will be used for team expansion, new architecture development, and product commercialization. The emergence of RWKV presents a strong challenge to the traditional Transformer architecture. With the advancement of large language models (LLMs), although the parameter scale of models is becoming increasingly large, issues like hallucination and accuracy persist.

In today's rapidly evolving technological landscape, Large Language Models (LLMs) play a crucial role across various industries, aiding in task automation and enhancing decision-making efficiency. However, in specialized fields like chip design, these models face unique challenges. NVIDIA's recently launched ChipAlign is specifically designed to address these challenges, aiming to combine the strengths of general-purpose LLMs with those of chip-specific LLMs. ChipAlign employs a new model merging strategy that...

Generative language models face many challenges during the transition from training to practical applications. One major issue is how to achieve optimal performance during the inference stage. Current strategies, such as Reinforcement Learning from Human Feedback (RLHF), primarily focus on improving the model's win rate but often overlook decoding strategies during inference, such as Best-of-N sampling and controlled decoding. This gap between training objectives and actual usage may lead to inefficiencies and affect the quality and reliability of outputs. To address these issues, Google D

MRI images have long been a significant challenge in medical imaging analysis due to their complexity and large data volume. To train large language models (LLMs) for MRI analysis, developers have had to slice the captured images into 2D images. While this approach is feasible, it limits the model's ability to analyze complex anatomical structures, particularly in intricate cases such as brain tumors, skeletal diseases, or cardiovascular conditions. Image source note: Image generated by AI, licensed by Midjourney. However, GE Healthcare this year...

On December 19, 2024, at a press conference, ZhiYuan Research Institute and Tencent announced the launch of LongBench v2, a benchmark designed to evaluate the deep understanding and reasoning capabilities of large language models (LLMs) in real-world long text multi-task scenarios. The platform aims to advance long text models in understanding and reasoning, addressing the current challenges faced by large language models in practical applications.

As one of the most watched AI startups outside of OpenAI and Anthropic, Cohere was valued at $5.5 billion in July. One of the company's co-founders is an author of the paper 'Attention Is All You Need,' which is considered critical to sparking the revolution of large language models (LLMs). Headquartered in Toronto and San Francisco, Cohere focuses on providing AI solutions for enterprise clients, rather than pursuing a similar path as other companies.

Large Language Models (LLMs) have made significant progress in the field of Natural Language Processing (NLP), shining in applications such as text generation, summarization, and question answering. However, the reliance of LLMs on token-level processing (predicting one word at a time) presents some challenges. This method contrasts with human communication, which typically operates at higher levels of abstraction, such as sentences or ideas. Token-level modeling also struggles in tasks that require understanding long contexts and may produce inconsistent outputs.

In recent years, large language models (LLMs) have made significant progress in the field of natural language processing (NLP), widely applicable in scenarios such as text generation, summarization, and question answering. However, these models rely on a token-level processing method that predicts word by word, which struggles with contextual understanding and often leads to inconsistent outputs. Moreover, when scaling LLMs to multilingual and multimodal applications, the computational costs and data requirements tend to be relatively high. To address these issues, Meta AI has proposed a novel approach.

In the age of AI, large language models (LLMs) are akin to martial arts secrets; their training processes consume massive computing power and data, much like martial arts experts who have spent years in secluded training. The release of open-source models is like these experts sharing their secrets with the public, but they come with certain licenses (such as Apache 2.0 and LLaMA 2 Community License) to protect their intellectual property (IP). However, the world can be treacherous, and incidents of 'shelling' are bound to occur. Some developers claim they have trained new LLMs when, in fact, they are using other foundational models (like Lla

Harvard University announced on Thursday the release of a high-quality dataset containing nearly one million public domain books, which anyone can use to train large language models and other AI tools. This dataset was created by Harvard's newly established Institutional Data Initiative and is funded by Microsoft and OpenAI. The included books are all works scanned by the Google Books Project that are no longer under copyright protection.

Alibaba DAMO Academy, in collaboration with the ModelScope community, recently announced the open sourcing of a new multilingual benchmark testing set, P-MMEval, aimed at comprehensively evaluating the multilingual capabilities of Large Language Models (LLMs) and conducting comparative analysis of cross-language transfer abilities. This testing set covers efficient datasets for both basic and specialized capabilities, ensuring consistency in multilingual coverage across all selected datasets, and provides parallel samples across multiple languages, supporting up to 10 languages from 8 different language families, including English, Chinese, and Arabic.

Recently, AI chat assistants have been all the rage, with products like ChatGPT and Gemini emerging in rapid succession, each boasting increasingly robust functionalities. Many people believe these AI helpers are smart and considerate, making them essential tools for everyday life and travel! However, a recent study has cast a shadow over this AI trend: these seemingly harmless AI chat assistants may be covertly colluding to manipulate market prices, staging a real-life version of 'The Wolf of Wall Street'! This research comes from a team of economists at Penn State University.