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Large language models (LLMs) have made impressive advancements in generating coherent text for various activities and domains, including grammatical error correction (GEC), text simplification, paraphrasing, and style transfer. One of the emerging skills of LLMs is their ability to generalize and perform tasks that they have never seen before. To achieve this, LLMs are fine-tuned…

In the enchanting world of language models and attention mechanisms, picture a daring quest to accelerate decoder inference and enhance the prowess of large language models. Our tale unfolds with the discovery of multi-query attention (MQA), a captivating technique that promises speedier results. Multi-query attention (MQA) expedites decoder inference through the employment of a single…

In the past year, large vision language models (LVLMs) have become a prominent focus in artificial intelligence research. When prompted differently, these models show promising performance across various downstream tasks. However, there’s still significant potential for improvement in LVLMs’ image perception capabilities.  Enhanced perceptual abilities for visual concepts are crucial for advancing model development and…

Heuristic algorithms are those algorithms that use practical and intuitive approaches to find solutions. They are very useful in making quick and effective decisions, even in the case of complex operational scenarios, such as managing servers in cloud environments. But, managing the reliability and efficiency of these heuristics is challenging for cloud operators. If not…

One of the most exciting advancements in AI and machine learning has been speech generation using Large Language Models (LLMs). While effective in various applications, the traditional methods face a significant challenge: the integration of semantic and perceptual information, often resulting in inefficiencies and redundancies. This is where SpeechGPT-Gen, a groundbreaking method introduced by researchers…

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…