Large language models (LLMs) are essential for natural language processing (NLP), but they demand significant computational resources and time for training. This requirement presents a key challenge in both research and application of LLMs. The challenge lies in efficiently training these huge models without compromising their performance.
Several approaches have been developed to address this issue.…
Large Language Models (LLMs) have proven highly competent in generating and understanding natural language, thanks to the vast amounts of data they're trained on. Predominantly, these models are used with general-purpose corpora, like Wikipedia or CommonCrawl, which feature a broad array of text. However, they sometimes struggle to be effective in specialized domains, owing to…
Large Language Models (LLMs) are typically trained on large swaths of data and demonstrate effective natural language understanding and generation. Unfortunately, they can often fail to perform well in specialized domains due to shifts in vocabulary and context. Seeing this deficit, researchers from NASA and IBM have collaborated to develop a model that covers multidisciplinary…
Peptides are involved in various biological processes and are instrumental in the development of new therapies. Understanding their conformations, i.e., the way they fold into their specific three-dimensional structures, is critical for their functional exploration. Despite the advancements in modeling protein structures, like with Google's AI system AlphaFold, the dynamic conformations of peptides remain challenging…
The advancement of deep generative models has brought new challenges in denoising, specifically in blind denoising where noise level and covariance are unknown. To tackle this issue, a research team from Ecole Polytechnique, Institut Polytechnique de Paris, and Flatiron Institute developed a novel method called the Gibbs Diffusion (GDiff) approach.
The GDiff approach is a fresh…
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…