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With new releases and introductions in the field of Artificial Intelligence (AI), Large Language Models (LLMs) are advancing significantly. They are showcasing their incredible capability of generating and comprehending natural language. However, there are certain difficulties experienced by LLMs with an emphasis on English when managing non-English languages, especially those with constrained resources. Although the…

The evolution of language models is a critical component in the dynamic field of natural language processing. These models, essential for emulating human-like text comprehension and generation, are instrumental in various applications, from translation to conversational interfaces. The core challenge tackled in this area is refining model efficiency, particularly in managing lengthy data sequences. Traditional…

With textual materials comprising a large portion of its content, the web is a continuously growing repository of real-world knowledge. Changes to information necessitate either the inclusion of new documents or revisions to older ones. This allows for the coexistence and eventual growth of numerous versions of information across different historical periods. Ensuring people can…

Fitness landscapes, a concept in evolutionary biology, represent how genetic variations influence an organism’s survival and reproductive success. They are formed by mapping genotypes to fitness, a measure of an organism’s ability to thrive and reproduce. These landscapes are central to understanding evolutionary processes and advancements in protein engineering. However, mapping these landscapes involves assessing…

In the evolving landscape of artificial intelligence and natural language processing, utilizing large language models (LLMs) has become increasingly prevalent. However, one of the challenges that persist in this domain is enabling these models to engage in role-play effectively. This work requires a deep understanding of language and an ability to embody diverse characters consistently….

Language models (LMs) often struggle with reasoning tasks like math or coding, particularly in low-resource languages. This challenge arises because LMs are primarily trained on data from a few high-resource languages, leaving low-resource languages underrepresented.  Previously, researchers have addressed this by continually training English-centric LMs on target languages. However, this method is difficult to scale…

Recent advances in language models showcase impressive zero-shot voice conversion (VC) capabilities. Nevertheless, prevailing VC models rooted in language models usually utilize offline conversion from source semantics to acoustic features, necessitating the entirety of the source speech and limiting their application to real-time scenarios. In this research, a team of researchers from Northwestern Polytechnical University,…

Vision-language models (VLMs) are increasingly prevalent, offering substantial advancements in AI-driven tasks. However, one of the most significant limitations of these advanced models, including prominent ones like GPT-4V, is their constrained spatial reasoning capabilities. Spatial reasoning involves understanding objects’ positions in three-dimensional space and their spatial relationships with one another. This limitation is particularly pronounced…

There is a need to build systems that can respond to user inputs, remember past interactions, and make decisions based on that history. This requirement is crucial for creating applications that behave more like intelligent agents, capable of maintaining a conversation, remembering past context, and making informed decisions. Currently, some solutions address parts of this…

With the growth of trending AI applications, Machine Learning ML models are being used for various purposes, leading to an increase in the advent of multimodal models. Multimodal models are very useful, and researchers are putting a lot of emphasis on these nowadays as they help mirror the complexity of human cognition by integrating diverse…