Training large language models (LLMs) hinges on the availability of diverse and abundant datasets, which can be created through synthetic data generation. The conventional methods of creating synthetic data - instance-driven and key-point-driven - have limitations in diversity and scalability, making them insufficient for training advanced LLMs.
Addressing these shortcomings, researchers at Tencent AI Lab have…
MultiOn AI has recently unveiled its latest development, the Retrieve API. This innovative autonomous web information retrieval API is designed to transform how businesses and developers extract and utilize data from the web. The API is an enhancement of the previously introduced Agent API and offers an all-encompassing solution for autonomous web browsing and data…
In the quick-paced field of artificial intelligence (AI), GPT4All 3.0, a milestone project by Nomic, is revolutionizing how large language models (LLMs) are accessed and controlled. As corporate control over AI intensifies, there emerges a higher demand for locally-run, open-source alternatives that prioritize user privacy and control. Addressing this demand, GPT4All 3.0 provides a comprehensive…
In a significant reveal that has shaken the world of technology, Kyutai introduced Moshi, a pioneering real-time native multimodal foundation model. This new AI model emulates and exceeds some functionalities previously demonstrated by OpenAI’s GPT-4o. Moshi understands and delivers emotions in various accents, including French, and can simultaneously handle two audio streams, allowing it to…
Concept-based learning (CBL) is a machine learning technique that involves using high-level concepts derived from raw features to make predictions. It enhances both model interpretability and efficiency. Among the various types of CBLs, the concept-based bottleneck model (CBM) has gained prominence. It compresses input features into a lower-dimensional space, capturing the essential data and discarding…
Large Language Models (LLMs) like GPT-3.5 Turbo and Mistral 7B often struggle to maintain accuracy while retrieving information from the middle of long input contexts, a phenomenon referred to as "lost-in-the-middle". This complication significantly hampers their effectiveness in tasks requiring the processing and reasoning over long passages, such as multi-document question answering (MDQA) and flexible…
Safeguarding user interactions with Language Models (LLMs) is an important aspect of artificial intelligence, as these models can produce harmful content or fall victim to adversarial prompts if not properly secured. Existing moderating tools, like Llama-Guard and various open-source models, focus primarily on identifying harmful content and assessing safety but suffer from shortcomings such as…
Business data analysis is an essential tool in modern companies, extracting actionable insights from large datasets to help maintain a competitive edge through informed decision-making. However, the combination of traditional rule-based systems and AI models can present challenges, often leading to inefficiencies and inaccuracies.
Despite rule-based systems being recognized for their reliability and precision, they can…
Scientists from The Hong Kong University of Science and Technology, and the University of Illinois Urbana-Champaign, have presented ScaleBiO, a unique bilevel optimization (BO) method that can scale up to 34B large language models (LLMs) on data reweighting tasks. The method relies on memory-efficient training technique called LISA and utilizes eight A40 GPUs.
BO is attracting…
Researchers from Shanghai Jiaotong University, Shanghai AI Laboratory, and Nanyang Technological University's S-Lab have developed an advanced multi-modal large language model (MLLM) called MG-LLaVA. This new model aims to overcome the limitations of current MLLMs when interpreting low-resolution images.
The main challenge with existing MLLMs has been their reliance on low-resolution inputs which compromises their…
Large Language Models (LLMs) have demonstrated impressive performances in numerous tasks, particularly classification tasks, in recent years. They exhibit a high degree of accuracy when provided with the correct answers or "gold labels". However, if the right answer is deliberately left out, these models tend to select an option from the available choices, even when…