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Language Model

Nvidia AI introduces ChatQA 2: A model based on Llama3 for improved comprehension of extended context and enhanced RAG abilities.

The field of large language models (LLMs) is developing at a rapid pace due to the need to process extensive text inputs and deliver accurate, efficient responses. Open-access LLMs and proprietary models like GPT-4-Turbo must handle substantial amounts of information that often exceed a single prompt’s limitations. This is key for tasks like document summarisation,…

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TaskGen: A Publicly Available Agentic Structure Using AI Agent to Tackle Any Task by Dividing it into Smaller Tasks.

The existing Artificial Intelligence (AI) task management methods, including AutoGPT, BabyAGI, and LangChain, often rely on free-text outputs, which can be lengthy and inefficient. These frameworks commonly struggle with keeping context and managing the extensive action space linked with arbitrary tasks. This report focuses on the inefficiencies of these current agentic frameworks, particularly in handling…

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Researchers at Amazon have suggested a novel approach to evaluate the accuracy of retrieval-enhanced large language models (RAG) relative to individual tasks.

Large language models (LLMs) have gained significant popularity recently, but evaluating them can be quite challenging, particularly for highly specialised client tasks requiring domain-specific knowledge. Therefore, Amazon researchers have developed a new evaluation approach for Retrieval-Augmented Generation (RAG) systems, focusing on such systems' factual accuracy, defined as their ability to retrieve and apply correct information…

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Researchers at Apple suggest LazyLLM: a unique AI strategy for productive LLM inference, specifically in situations with extended context.

Large Language Models (LLMs) have improved significantly, but challenges persist, particularly in the prefilling stage. This is because the cost of computing attention increases with the number of tokens in the prompts, leading to a slow time-to-first-token (TTFT). As such, optimizing TTFT is crucial for efficient LLM inference. Various methods have been proposed to improve…

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OAK (Open Artificial Knowledge) Dataset: An Extensive Tool for AI Studies Sourced from Wikipedia’s Primary Sections

The significant progress in Artificial Intelligence (AI) and Machine Learning (ML) has underscored the crucial need for extensive, varied, and high-quality datasets to train and test basic models. Gathering such datasets is a challenging task due to issues like data scarcity, privacy considerations, and expensive data collection and annotation. Synthetic or artificial data has emerged…

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An AI research paper from UC Berkeley outlines that coupling GPT with Prolog, a dependable symbolic system, significantly enhances its capacity to solve mathematical problems.

Researchers from the University of California, Berkeley, have recently shed light on developing the performance of large language models (LLMs) in the field of Natural Language Processing (NLP). In spite of showing a high degree of language comprehension, LLMs display limitations in reliable and flexible reasoning. This can be attributed to the structural operation of…

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Microsoft Research unveils E5-V: a comprehensive AI model for multimodal embeddings, using single-modality training for text pairs.

Multimodal Large Language Models (MLLM) represent a significant advancement in the field of artificial intelligence. Unifying verbal and visual comprehension, MLLMs enhance understanding of the complex relationships between various forms of media. They also dictate how these models manage elaborate tasks that require comprehension of numerous types of data. Given their importance, MLLMs are now…

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