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.

Nexa AI recently unveiled its new OmniAudio-2.6B audio language model, designed to meet the efficient deployment demands of edge devices. Unlike traditional architectures that separate automatic speech recognition (ASR) and language models, OmniAudio-2.6B integrates Gemma-2-2b, Whisper Turbo, and a custom projector into a unified framework. This design eliminates the inefficiencies and delays associated with linking various components in traditional systems, making it especially suitable for resource-constrained computing.

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.

The Intelligent AI Assistant Doubao, developed by ByteDance, has recently upgraded its text-to-image capabilities. The new feature allows users to generate images containing specified text with a single click. The relevant person in charge of the Doubao large model team pointed out that the text-to-image model of Doubao integrates LLM (Large Language Model) and DIT (Digital Image Technology) framework, optimizing its learning ability for Chinese data, and enhancing the generation capability of Chinese characters, significantly improving the generation results.

In recent years, large language models represented by ChatGPT have sparked a new wave in the AI field. These powerful language models can not only understand human instructions but also formulate plans, explore environments, and utilize tools to solve complex tasks, demonstrating immense potential in areas such as robotics, personal assistants, and process automation. However, most existing AI entity systems are passive, requiring explicit human instructions to perform tasks. To schedule a meeting, one still has to manually input the time, location, and even list out all the participants, which is quite cumbersome.

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.

Amazon has been reported to be developing a multi-modal large language model named 'Olympus', which is expected to be officially announced at the AWS re:Invent conference next week. According to The Information, the internal code name for this algorithm is 'Olympus'. Last November, Reuters reported that Amazon had invested millions of dollars in training a large language model named 'Olympus', with a parameter count reaching 2 trillion.

Teuken-7B, a language model with 7 billion parameters, has now been launched on Hugging Face, supporting all 24 official EU languages. This model was developed by the EU OpenGPT-X research project and is available as an open-source project for users. Unlike most AI language models that are English-centric, Teuken-7B was built from the ground up, with approximately half of its training data sourced from non-English European languages. Image source note: The image is AI-generated.

Non-profit AI research organization Ai2 recently launched its new OLMo2 series, the second generation of its 'Open Language Model' (OLMo) series. The release of OLMo2 not only provides strong technical support for the AI community but also represents the latest development in open source AI with its completely open-source code. Unlike other 'open' language models currently on the market, such as Meta's Llama series, OLMo2 meets the strict definition of the open-source initiative, which means that the materials used for its development...

Recently, Meta AI open-sourced a foundational multimodal language model named SPIRIT LM, which can freely mix text and speech, opening new possibilities for multimodal tasks involving audio and text. SPIRIT LM is based on a pre-trained text language model with 7 billion parameters, which has been continuously trained on text and speech units, expanding into the speech modality. It can understand and generate text like a large text model, while also being capable of understanding and generating speech, and even mixing text and speech to create various forms of expression.

Recently, according to Bloomberg, Apple is developing a new voice assistant, Siri, which will utilize advanced large language models (LLMs) technology to achieve a more natural conversational experience. This move aims to close the gap with competitors, as products like Google's Gemini Live have demonstrated higher naturalness in conversational capabilities in recent years. According to sources, the new Siri assistant will completely replace the current Siri interface used by customers, with a planned release in 2026.

With the rapid development of artificial intelligence (AI) technologies in industrial sectors, experts indicate that high-quality data and data governance will be more important than generative technologies. By 2025, companies must focus more on scalable and flexible solutions when adopting AI, rather than solely relying on Generative AI (GenAI). Image note: The picture is generated by AI, authorized by the image service provider Midjourney, based on the analysis by Qlik, the key to fully harnessing AI's potential lies in companies' investments in high-quality, real-time data.