Pretrained language models (LMs) are essential tools in the realm of machine learning, often used for a variety of tasks and domains. But, adapting these models, also known as finetuning, can be expensive and time-consuming, especially for larger models. Traditionally, the solution to this issue has been to use Parameter-efficient finetuning (PEFT) methods such as…
Generative Artificial Intelligence (AI) has seen significant advancement in different fields like art, content creation, and entertainment by leveraging machine learning algorithms. AI programs can now generate various forms of content, such as images, music, text, and videos. This paradigm shift has enabled a novel, realistic, and diverse range of outputs, transforming the creative process.
Concerning…
Developers, project managers, and business owners often face the challenge of swiftly converting conceptual ideas into interactive, tangible prototypes. This process typically requires extensive programming knowledge, even with the aid of tools such as integrated development environments (IDEs) and software development kits (SDKs), and can be time-consuming and excluding for non-technical stakeholders. This lack of…
The rapid improvement of large language models and their role in natural language processing has led to challenges in incorporating less commonly spoken languages. Embedding the majority of artificial intelligence (AI) systems in well-known languages inevitably forces a technological divide across linguistic communities that remains mostly unaddressed.
This paper introduces the SambaLingo system, a novel…
Digital agents, or software designed to streamline interactions between humans and digital platforms, are becoming increasingly popular due to their potential to automate routine tasks. However, a consistent challenge with these agents is their frequent misunderstanding of user commands or inability to adapt to new or unique environments—problems that can lead to errors and inefficiency.…
Large-scale language models (LLMs) have made substantial progress in understanding language by absorbing information from their environment. However, while they excel in areas like historical knowledge and insightful responses, they struggle when it comes to real-time comprehension. Embodied AI, integrated into items like smart glasses or home robots, aims to interact with humans using everyday…
Memory is a crucial component of intelligence, facilitating the recall and application of past experiences to current situations. However, both traditional Transformer models and Transformer-based Large Language Models (LLMs) have limitations related to context-dependent memory due to the workings of their attention mechanisms. This primarily concerns the memory consumption and computation time of these attention…
Scientific research, despite its vital role in improving human well-being, often grapples with challenges due to its complexities and the slow progress it typically makes. This often necessitates specialized expertise. The application of artificial intelligence (AI), especially large language models (LLMs) is identified as a potential game-changer in the process of scientific research. LLMs have…
Recent research in Artificial Intelligence (AI) has shown a growing interest in the capabilities of large language models (LLMs) due to their versatility and adaptability. These models, traditionally used for tasks in natural language processing, are now being explored for potential use in computational tasks, such as regression analysis. The idea behind this exploration is…
Chain-of-thought (CoT) prompting, an instruction method for language models (LMs), seeks to improve a model's performance across arithmetic, commonsense, and symbolic reasoning tasks. However, it falls short in larger models (with over 100 billion parameters) due to its repetitive rationale and propensity to produce unaligned rationales and answers.
Researchers from Penn State University and Amazon AGI…