Language Agents represent a transformative advancement in computational linguistics. They leverage large language models (LLMs) to interact with and process information from the external world. Through innovative use of tools and APIs, these agents autonomously acquire and integrate new knowledge, demonstrating significant progress in complex reasoning tasks. A critical challenge in Language Agents is managing…
Deploying dense retrieval models is crucial in industries like enterprise search (ES), where a single service supports multiple enterprises. In ES, such as the Cloud Customer Service (CCS), personalized search engines are generated from uploaded business documents to assist customer inquiries. The success of ES providers relies on delivering time-efficient searching customization to meet scalability…
Though it has always played an essential part in natural language processing, textual data processing now sees new uses in the field. This is especially true when it comes to LLMs’ function as generic interfaces; these interfaces take examples and general system instructions, tasks, and other specifications expressed in natural language. As a result, there…
A team of researchers associated with Peking University, Pika, and Stanford University has introduced RPG (Recaption, Plan, and Generate). The proposed RPG framework is the new state-of-the-art in the context of text-to-image conversion, especially in handling complex text prompts involving multiple objects with various attributes and relationships. The existing models which have shown exceptional results…
Current multi-modal language models (LMs) face limitations in performing complex visual reasoning tasks. These tasks, such as compositional action recognition in videos, demand an intricate blend of low-level object motion and interaction analysis with high-level causal and compositional spatiotemporal reasoning. While these models excel in various areas, their effectiveness in tasks requiring detailed attention to…
Artificial Intelligence (AI), particularly through deep learning, has revolutionized many fields, including machine translation, natural language understanding, and computer vision. The field of medical imaging, specifically chest X-ray (CXR) interpretation, is no exception. CXRs, the most frequently performed diagnostic imaging tests, hold immense clinical significance. The advent of vision-language foundation models (FMs) has opened new…
The evolution of technology in speech recognition has been marked by significant strides, but challenges like latency the time delay in processing spoken language, have continually impeded progress. This latency is especially pronounced in autoregressive models, which process speech sequentially, leading to delays. These delays are detrimental in real-time applications like live captioning or virtual…
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…
