With the rise of Large Language Models (LLMs) in recent years, generative AI has made significant strides in the field of language processing, showcasing impressive abilities in a wide array of tasks. Given their potential in solving complex tasks, researchers have made quite a number of attempts to apply these models in the field of…
For LLMs, auto-regressive decoding is now considered the gold standard. Because LLMs generate output tokens individually, the procedure is time-consuming and expensive. Methods based on speculative sampling provide an answer to this problem. In the first, called the “draft” phase, LLMs are hypothesized at little cost; in the second, called the “verification” phase, all of…
In computational linguistics and artificial intelligence, researchers continually strive to optimize the performance of large language models (LLMs). These models, renowned for their capacity to process a vast array of language-related tasks, face significant challenges due to their expansive size. For instance, models like GPT-3, with 175 billion parameters, require substantial GPU memory, highlighting a…
In the realm of artificial intelligence, Large Multimodal Models (LMMs) have exhibited remarkable problem-solving capabilities across diverse tasks, such as zero-shot image/video classification, zero-shot image/video-text retrieval, and multimodal question answering (QA). However, recent studies highlight a substantial gap between powerful LMMs and expert-level artificial intelligence, particularly in tasks involving complex perception and reasoning with domain-specific…
In the dynamic field of software development, integrating large language models (LLMs) has initiated a new chapter, especially in code intelligence. These sophisticated models have been pivotal in automating various aspects of programming, from identifying bugs to generating code, revolutionizing how coding tasks are approached and executed. The impact of these models is vast, offering…
Evaluating LLMs as versatile agents is crucial for their integration into practical applications. However, existing evaluation frameworks face challenges in benchmarking diverse scenarios, maintaining partially observable environments, and capturing multi-round interactions. Current assessments often focus on a simplified final success rate metric, providing limited insights into the complex processes. The complexity of agent tasks, involving…
Transformers have found widespread application in diverse tasks spanning text classification, map construction, object detection, point cloud analysis, and audio spectrogram recognition. Their versatility extends to multimodal tasks, exemplified by CLIP’s use of image-text pairs for superior image recognition. This underscores transformers’ efficacy in establishing universal sequence-to-sequence modeling, creating embeddings that unify data representation across…
Large language models (LLMs) based on transformer architectures have emerged in recent years. Models such as Chat-GPT and LLaMA-2 demonstrate how the parameters of LLMs have rapidly increased, ranging from several billion to tens of trillions. Although LLMs are very good generators, they have trouble with inference delay since there is a lot of computing…
Artificial intelligence is widely useful in environment-related fields. Recently, there has been increasing research on using AI in carbon capture technology. Carbon capture technology is critical in tackling climate change by trapping carbon dioxide (CO2) emissions from power plants. However, the current carbon capture systems are inefficient and may consume significant energy. Consequently, researchers from…
One of the more intriguing developments in the dynamic field of computer vision is the efficient processing of visual data, which is essential for applications ranging from automated image analysis to the development of intelligent systems. A pressing challenge in this area is interpreting complex visual information, particularly in reconstructing detailed images from partial data….
